diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000..c72306bf37 Binary files /dev/null and b/.DS_Store differ diff --git a/data/.DS_Store b/data/.DS_Store new file mode 100644 index 0000000000..1d93d0b6a9 Binary files /dev/null and b/data/.DS_Store differ diff --git a/data/3DSP_siggraphAsia2013.bib b/data/3DSP_siggraphAsia2013.bib deleted file mode 100644 index 04f1c70252..0000000000 --- a/data/3DSP_siggraphAsia2013.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article {li2013three, - Author = {Hao Li and Etienne Vouga and Anton Gudym and Linjie Luo and Jonathan T. Barron and Gleb Gusev}, - Title = {3D Self-Portraits}, - Journal = {SIGGRAPH Asia}, - Year = {2013}, -} \ No newline at end of file diff --git a/data/Anderson2016.bib b/data/Anderson2016.bib deleted file mode 100644 index 94bfe96c5d..0000000000 --- a/data/Anderson2016.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{Anderson2016, -author = {Robert Anderson and David Gallup and Jonathan T. Barron and Janne Kontkanen and Noah Snavely and Carlos Hern\'andez and Sameer Agarwal and Steven M Seitz}, -title = {Jump: Virtual Reality Video}, -journal = {SIGGRAPH Asia}, -year = {2016}, -} diff --git a/data/ArbelaezCVPR2014.bib b/data/ArbelaezCVPR2014.bib deleted file mode 100644 index 20a7cf4311..0000000000 --- a/data/ArbelaezCVPR2014.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{ArbelaezCVPR2014, - title = "Multiscale Combinatorial Grouping", - author = "Arbel{\'a}ez, Pablo and Pont-Tuset, J. and Barron, Jonathan T. and Marqu{\'e}s, F. and Malik, Jitendra", - journal = "CVPR", - year = "2014", -} diff --git a/data/B3DO_ICCV_2011.bib b/data/B3DO_ICCV_2011.bib deleted file mode 100644 index 792018696a..0000000000 --- a/data/B3DO_ICCV_2011.bib +++ /dev/null @@ -1,7 +0,0 @@ -@inproceedings{Janoch2011, -author = {Janoch, Allison and Karayev, Sergey and Jia, Yangqing and Barron, Jonathan T. and Fritz, Mario and Saenko, Kate and Darrell, Trevor}, -booktitle = {ICCV Workshop on Consumer Depth Cameras for Computer Vision}, -title = {A Category-Level 3-D Object Dataset: Putting the Kinect to Work}}, -year = {2011} -} - diff --git a/data/BarronCVPR2015.bib b/data/BarronCVPR2015.bib deleted file mode 100644 index 698635b6b2..0000000000 --- a/data/BarronCVPR2015.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{Barron2015A, -author = {Jonathan T. Barron and Andrew Adams and YiChang Shih and Carlos Hern\'andez}, -title = {Fast Bilateral-Space Stereo for Synthetic Defocus}, -journal = {CVPR}, -year = {2015}, -} diff --git a/data/BarronCVPR2019.bib b/data/BarronCVPR2019.bib deleted file mode 100644 index 51bbc86ae9..0000000000 --- a/data/BarronCVPR2019.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{BarronCVPR2019, - Author = {Jonathan T. Barron}, - Title = {A General and Adaptive Robust Loss Function}, - Journal = {CVPR}, - Year = {2019} -} \ No newline at end of file diff --git a/data/BarronCVPR2019_reviews.txt b/data/BarronCVPR2019_reviews.txt deleted file mode 100644 index 92bdd196cf..0000000000 --- a/data/BarronCVPR2019_reviews.txt +++ /dev/null @@ -1,16 +0,0 @@ -R1: - -My first impression is that this paper might be better reviewed in a machine -learning centered conference (i.e. ICML) instead of the computer vision ones, -as the major contribution of this paper comes from the loss function that could -be potentially applied to any learning task, not specific vision task. - -Borderline Reject - - -R2: - -Good theoretical development of unified robust loss and extensive experimental -evaluation on four vision task. - -Accept diff --git a/data/BarronECCV2020.bib b/data/BarronECCV2020.bib deleted file mode 100644 index 47bb19904a..0000000000 --- a/data/BarronECCV2020.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{barron2020generalization, - title={A Generalization of Otsu's Method and Minimum Error Thresholding}, - author={Barron, Jonathan T}, - journal={ECCV}, - year={2020} -} \ No newline at end of file diff --git a/data/BarronICCV2013.bib b/data/BarronICCV2013.bib deleted file mode 100644 index c670451032..0000000000 --- a/data/BarronICCV2013.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{BarronICCV2013, - AUTHOR = "Barron, Jonathan T. and Arbel{\'a}ez, Pablo and Ker{\"a}nen, Soile V. E. and Biggin, Mark D. and Knowles, David W. and Malik, Jitendra", - TITLE = "Volumetric Semantic Segmentation using Pyramid Context Features", - JOURNAL = "ICCV", - YEAR = "2013", -} diff --git a/data/BarronICCV2015.bib b/data/BarronICCV2015.bib deleted file mode 100644 index ae3fe68eea..0000000000 --- a/data/BarronICCV2015.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{BarronICCV2015, - Author = {Jonathan T. Barron}, - Title = {Convolutional Color Constancy}, - Journal = {ICCV}, - Year = {2015} -} diff --git a/data/BarronMalikCVPR2011.bib b/data/BarronMalikCVPR2011.bib deleted file mode 100644 index 3cbe3af3e5..0000000000 --- a/data/BarronMalikCVPR2011.bib +++ /dev/null @@ -1,7 +0,0 @@ -@article{Barron2011, -Title = {High-Frequency Shape and Albedo from Shading using Natural Image Statistics}, -Journal = {CVPR}, -Author = {Jonathan T. Barron and Jitendra Malik}, -Year = {2011}, -} - diff --git a/data/BarronMalikCVPR2012.bib b/data/BarronMalikCVPR2012.bib deleted file mode 100644 index 5170eed5fd..0000000000 --- a/data/BarronMalikCVPR2012.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{Barron2012A, -Title = {Shape, Albedo, and Illumination from a Single Image of an Unknown Object}, -Journal = {CVPR}, -Author = {Jonathan T. Barron and Jitendra Malik}, -Year = {2012}, -} diff --git a/data/BarronMalikCVPR2013.bib b/data/BarronMalikCVPR2013.bib deleted file mode 100644 index 89fc4c600c..0000000000 --- a/data/BarronMalikCVPR2013.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{Barron2013A, -author = {Jonathan T. Barron and Jitendra Malik}, -title = {Intrinsic Scene Properties from a Single RGB-D Image}, -journal = {CVPR}, -year = {2013}, -} diff --git a/data/BarronMalikECCV2012.bib b/data/BarronMalikECCV2012.bib deleted file mode 100644 index 8a11354ab7..0000000000 --- a/data/BarronMalikECCV2012.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{Barron2012B, -author = {Jonathan T. Barron and Jitendra Malik}, -title = {Color Constancy, Intrinsic Images, and Shape Estimation}, -journal = {ECCV}, -year = {2012}, -} diff --git a/data/BarronMalikTPAMI2015.bib b/data/BarronMalikTPAMI2015.bib deleted file mode 100644 index 37937fac3b..0000000000 --- a/data/BarronMalikTPAMI2015.bib +++ /dev/null @@ -1,6 +0,0 @@ -@ARTICLE{BarronTPAMI2015, -Author = {Jonathan T. Barron and Jitendra Malik}, -Title = {Shape, Illumination, and Reflectance from Shading}, -journal={TPAMI}, -year={2015}, -} diff --git a/data/BarronMalikTPAMI2015B.bib b/data/BarronMalikTPAMI2015B.bib deleted file mode 100644 index 041cd96724..0000000000 --- a/data/BarronMalikTPAMI2015B.bib +++ /dev/null @@ -1,6 +0,0 @@ -@ARTICLE{BarronTPAMI2015B, -Author = {Jonathan T. Barron and Jitendra Malik}, -title = {Intrinsic Scene Properties from a Single RGB-D Image}, -journal={TPAMI}, -year={2015}, -} diff --git a/data/BarronPRL2009.bib b/data/BarronPRL2009.bib deleted file mode 100644 index 6e4c687cc6..0000000000 --- a/data/BarronPRL2009.bib +++ /dev/null @@ -1,6 +0,0 @@ -@techreport{BarronPRL2009, - Author = {Jonathan T. Barron and Dave Golland and Nicholas J. Hay}, - Title = {Parallelizing Reinforcement Learning}, - Institution = {EECS Department, University of California, Berkeley}, - Year = {2009}, -} diff --git a/data/BarronPooleECCV2016.bib b/data/BarronPooleECCV2016.bib deleted file mode 100644 index 33c64bde77..0000000000 --- a/data/BarronPooleECCV2016.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{BarronPoole2016, -author = {Jonathan T. Barron and Ben Poole}, -title = {The Fast Bilateral Solver}, -journal = {ECCV}, -year = {2016}, -} diff --git a/data/BarronPooleECCV2016_reviews.txt b/data/BarronPooleECCV2016_reviews.txt deleted file mode 100644 index 0ac9437811..0000000000 --- a/data/BarronPooleECCV2016_reviews.txt +++ /dev/null @@ -1,13 +0,0 @@ -Essentially sections 1-3 contain very little new. Section 4 is marginally -interesting. Weak Reject. - - R2, CVPR 2016 - -the paper too much relies on the supplemental material compared with the -standard usage of the supplemental material. I suggest the authors to directly -go for a journal submission by reformatting the paper together with the -supplemental material. Strong Reject. - - R1, ECCV 2016 - -The paper solves a class of relevant problems in a neat manner. The method -should be made known to a wider audience. Oral. - - R3, ECCV 2016 \ No newline at end of file diff --git a/data/BarronTR2010.bib b/data/BarronTR2010.bib deleted file mode 100644 index 75f29d1265..0000000000 --- a/data/BarronTR2010.bib +++ /dev/null @@ -1,9 +0,0 @@ -@techreport{Barron:EECS-2010-94, - Author = {Barron, Jonathan T. and Malik, Jitendra}, - Title = {Discovering Efficiency in Coarse-To-Fine Texture Classification}, - Institution = {EECS Department, University of California, Berkeley}, - Year = {2010}, - Month = {Jun}, - URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-94.html}, - Number = {UCB/EECS-2010-94}, -} diff --git a/data/BarronTsaiCVPR2017.bib b/data/BarronTsaiCVPR2017.bib deleted file mode 100644 index 306bc2e2e2..0000000000 --- a/data/BarronTsaiCVPR2017.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{BarronTsai2017, -author = {Jonathan T. Barron and Yun-Ta Tsai}, -title = {Fast Fourier Color Constancy}, -journal = {CVPR}, -year = {2017}, -} diff --git a/data/BrooksBarronCVPR2019.bib b/data/BrooksBarronCVPR2019.bib deleted file mode 100644 index 4988e334d9..0000000000 --- a/data/BrooksBarronCVPR2019.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{BrooksBarronCVPR2019, - author = {Tim Brooks and Jonathan T. Barron}, - title = {Learning to Synthesize Motion Blur}, - journal = {CVPR}, - year = {2019}, -} \ No newline at end of file diff --git a/data/BrooksCVPR2019.bib b/data/BrooksCVPR2019.bib deleted file mode 100644 index 50ab85e9a1..0000000000 --- a/data/BrooksCVPR2019.bib +++ /dev/null @@ -1,11 +0,0 @@ -@article{BrooksCVPR2019, - author = {Tim Brooks and - Ben Mildenhall and - Tianfan Xue and - Jiawen Chen and - Dillon Sharlet and - Jonathan T. Barron}, - title = {Unprocessing Images for Learned Raw Denoising }, - journal = {CVPR}, - year = {2019}, -} \ No newline at end of file diff --git a/data/Chen2016.bib b/data/Chen2016.bib deleted file mode 100644 index ca5506a87c..0000000000 --- a/data/Chen2016.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{Chen2016, - author = {Liang{-}Chieh Chen and Jonathan T. Barron and George Papandreou and Kevin Murphy and Alan L. Yuille}, - title = {Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform}, - journal = {CVPR}, - year = {2016}, -} diff --git a/data/Diverdi2016.bib b/data/Diverdi2016.bib deleted file mode 100644 index 689ae4bab2..0000000000 --- a/data/Diverdi2016.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{Diverdi2016, -author={Stephen DiVerdi and Jonathan T. Barron}, -journal={WACV}, -title={Geometric Calibration for Mobile, Stereo, Autofocus Cameras}, -year={2016}, -} diff --git a/data/GargICCV2019.bib b/data/GargICCV2019.bib deleted file mode 100644 index e275d645f1..0000000000 --- a/data/GargICCV2019.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{GargICCV2019, - author = {Rahul Garg and Neal Wadhwa and Sameer Ansari and Jonathan T. Barron}, - title = {Learning Single Camera Depth Estimation using Dual-Pixels}, - journal = {ICCV}, - year = {2019}, -} \ No newline at end of file diff --git a/data/GharbiSIGGRAPH2017.bib b/data/GharbiSIGGRAPH2017.bib deleted file mode 100644 index a81c20180e..0000000000 --- a/data/GharbiSIGGRAPH2017.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{GharbiSIGGRAPH2017, -author = {Micha{\"e}l Gharbi and Jiawen Chen and Jonathan T. Barron and Samuel W. Hasinoff and Fr{\'e}do Durand}, -title = {Deep Bilateral Learning for Real-Time Image Enhancement}, -journal = {SIGGRAPH}, -year = {2017}, -} diff --git a/data/Hasinoff2016.bib b/data/Hasinoff2016.bib deleted file mode 100644 index d9cf04e301..0000000000 --- a/data/Hasinoff2016.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{Hasinoff2016, -author = {Samuel W. Hasinoff and Dillon Sharlet and Ryan Geiss and Andrew Adams and Jonathan T. Barron and Florian Kainz and Jiawen Chen and Marc Levoy}, -title = {Burst Photography for High Dynamic Range and Low-Light Imaging on Mobile Cameras}, -journal = {SIGGRAPH Asia}, -year = {2016}, -} diff --git a/data/JonBarron-CV.pdf b/data/JonBarron-CV.pdf deleted file mode 100644 index b7fd3a44a5..0000000000 Binary files a/data/JonBarron-CV.pdf and /dev/null differ diff --git a/data/JonBarron-bio.txt b/data/JonBarron-bio.txt deleted file mode 100644 index fb2c18af6f..0000000000 --- a/data/JonBarron-bio.txt +++ /dev/null @@ -1 +0,0 @@ -Jon Barron is a principal research scientist at Google DeepMind in San Francisco, where he works on computer vision and machine learning. He received a PhD in Computer Science from the University of California, Berkeley in 2013, where he was advised by Jitendra Malik, and he received a Honours BSc in Computer Science from the University of Toronto in 2007. He received a National Science Foundation Graduate Research Fellowship in 2009, the C.V. Ramamoorthy Distinguished Research Award in 2013, and the PAMI Young Researcher Award in 2020. His works have received awards at ECCV 2016, TPAMI 2016, ECCV 2020, ICCV 2021, CVPR 2022, the 2022 Communications of the ACM, ICLR 2023, and SIGGRAPH 2024. diff --git a/data/KarschCVPR2013.bib b/data/KarschCVPR2013.bib deleted file mode 100644 index 406b0b3c27..0000000000 --- a/data/KarschCVPR2013.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{Karsch2013A, -author = {Kevin Karsch and Zicheng Liao and Jason Rock and Jonathan T. Barron and Derek Hoiem}, -title = {Boundary Cues for 3D Object Shape Recovery}, -journal = {CVPR}, -year = {2013}, -} diff --git a/data/Mildenhall2018.bib b/data/Mildenhall2018.bib deleted file mode 100644 index 192ae77eb3..0000000000 --- a/data/Mildenhall2018.bib +++ /dev/null @@ -1,11 +0,0 @@ -@article{Mildenhall2018, - author = {Ben Mildenhall and - Jonathan T. Barron and - Jiawen Chen and - Dillon Sharlet and - Ren Ng and - Robert Carroll}, - title = {Burst Denoising with Kernel Prediction Networks}, - journal = {CVPR}, - year = {2018}, -} \ No newline at end of file diff --git a/data/PontTusetTPAMI2017.bib b/data/PontTusetTPAMI2017.bib deleted file mode 100644 index d70ab8bf64..0000000000 --- a/data/PontTusetTPAMI2017.bib +++ /dev/null @@ -1,9 +0,0 @@ -@article{Pont-Tuset2017, - author = {J. Pont-Tuset and P. Arbel\'{a}ez and Jonathan T. Barron and F. Marques and J. Malik}, - title = {Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation}, - journal = {TPAMI}, - year = {2017}, - volume = {39}, - number = {1}, - pages = {128 - 140} -} diff --git a/data/Pranamya_Resume.pdf b/data/Pranamya_Resume.pdf new file mode 100644 index 0000000000..eb71ea7fa4 Binary files /dev/null and b/data/Pranamya_Resume.pdf differ diff --git a/data/Shelhamer2015.bib b/data/Shelhamer2015.bib deleted file mode 100644 index 9c91ae86d9..0000000000 --- a/data/Shelhamer2015.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{Shelhamer2015, -author={Evan Shelhamer and Jonathan T. Barron and Trevor Darrell}, -journal={ICCV Workshop}, -title={Scene Intrinsics and Depth from a Single Image}, -year={2015}, -} diff --git a/data/Srinivasan2018.bib b/data/Srinivasan2018.bib deleted file mode 100644 index 8e8effac37..0000000000 --- a/data/Srinivasan2018.bib +++ /dev/null @@ -1,10 +0,0 @@ -@article{Srinivasan2018, - author = {Pratul P. Srinivasan and - Rahul Garg and - Neal Wadhwa and - Ren Ng and - Jonathan T. Barron}, - title = {Aperture Supervision for Monocular Depth Estimation}, - journal = {CVPR}, - year = {2018}, -} \ No newline at end of file diff --git a/data/SrinivasanCVPR2019.bib b/data/SrinivasanCVPR2019.bib deleted file mode 100644 index 12092c7496..0000000000 --- a/data/SrinivasanCVPR2019.bib +++ /dev/null @@ -1,11 +0,0 @@ -@article{SrinivasanCVPR2019, - author = {Pratul P. Srinivasan and - Richard Tucker and - Jonathan T. Barron and - Ravi Ramamoorthi and - Ren Ng and - Noah Snavely}, - title = {Pushing the Boundaries of View Extrapolation with Multiplane Images} - journal = {CVPR}, - year = {2019}, -} \ No newline at end of file diff --git a/data/SunSIGGRAPH2019.bib b/data/SunSIGGRAPH2019.bib deleted file mode 100644 index 8013397d01..0000000000 --- a/data/SunSIGGRAPH2019.bib +++ /dev/null @@ -1,15 +0,0 @@ -@article{Sun2019, - author = {Tiancheng Sun and - Jonathan T. Barron and - Yun{-}Ta Tsai and - Zexiang Xu and - Xueming Yu and - Graham Fyffe and - Christoph Rhemann and - Jay Busch and - Paul E. Debevec and - Ravi Ramamoorthi}, - title = {Single Image Portrait Relighting}, - journal = {SIGGRAPH}, - year = {2019}, -} \ No newline at end of file diff --git a/data/Valentin2018.bib b/data/Valentin2018.bib deleted file mode 100644 index 4bd2a2a113..0000000000 --- a/data/Valentin2018.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{Valentin2018, - author = {Valentin, Julien and Kowdle, Adarsh and Barron, Jonathan T. and Wadhwa, Neal and Dzitsiuk, Max and Schoenberg, Michael and Verma, Vivek and Csaszar, Ambrus and Turner, Eric and Dryanovski, Ivan and Afonso, Joao and Pascoal, Jose and Tsotsos, Konstantine and Leung, Mira and Schmidt, Mirko and Guleryuz, Onur and Khamis, Sameh and Tankovitch, Vladimir and Fanello, Sean and Izadi, Shahram and Rhemann, Christoph}, - title = {Depth from Motion for Smartphone AR}, - journal = {SIGGRAPH Asia}, - year = {2018}, -} diff --git a/data/Wadhwa2018.bib b/data/Wadhwa2018.bib deleted file mode 100644 index 46141b54b4..0000000000 --- a/data/Wadhwa2018.bib +++ /dev/null @@ -1,6 +0,0 @@ -@article{Wadhwa2018, - title={Synthetic Depth-of-Field with a Single-Camera Mobile Phone}, - author={Wadhwa, Neal and Garg, Rahul and Jacobs, David E. and Feldman, Bryan E. and Kanazawa, Nori and Carroll, Robert and Movshovitz-Attias, Yair and Barron, Jonathan T. and Pritch, Yael and Levoy, Marc}, - journal={SIGGRAPH}, - year={2018}, -} \ No newline at end of file diff --git a/data/cvpr2023_llm_workshop_annotated.pdf b/data/cvpr2023_llm_workshop_annotated.pdf deleted file mode 100644 index 21b28522fb..0000000000 Binary files a/data/cvpr2023_llm_workshop_annotated.pdf and /dev/null differ diff --git a/data/ieee_fullfirstname.bst b/data/ieee_fullfirstname.bst deleted file mode 100644 index d9ca122f6e..0000000000 --- a/data/ieee_fullfirstname.bst +++ /dev/null @@ -1,1135 +0,0 @@ -% This is a modification to the normal ieee.bst used by CVPR to render -% first names in the bibliography as "Firstname Lastname" rather than -% "F. Lastname". -% -% Jonathan T. Barron, 12/5/2018, jonbarron@gmail.com - -% --------------------------------------------------------------- -% -% ieee.bst,v 1.0 2002/04/16 -% -% by Glenn Paulley (paulley@acm.org) -% -% Modified from latex8.bst 1995/09/15 15:13:49 ienne Exp $ -% -% by Paolo.Ienne@di.epfl.ch -% -% -% --------------------------------------------------------------- -% -% no guarantee is given that the format corresponds perfectly to -% IEEE 8.5" x 11" Proceedings, but most features should be ok. -% -% --------------------------------------------------------------- -% -% `ieee' from BibTeX standard bibliography style `abbrv' -% version 0.99a for BibTeX versions 0.99a or later, LaTeX version 2.09. -% Copyright (C) 1985, all rights reserved. -% Copying of this file is authorized only if either -% (1) you make absolutely no changes to your copy, including name, or -% (2) if you do make changes, you name it something other than -% btxbst.doc, plain.bst, unsrt.bst, alpha.bst, and abbrv.bst. -% This restriction helps ensure that all standard styles are identical. -% The file btxbst.doc has the documentation for this style. - -ENTRY - { address - author - booktitle - chapter - edition - editor - howpublished - institution - journal - key - month - note - number - organization - pages - publisher - school - series - title - type - volume - year - } - {} - { label } - -INTEGERS { output.state before.all mid.sentence after.sentence after.block } - -FUNCTION {init.state.consts} -{ #0 'before.all := - #1 'mid.sentence := - #2 'after.sentence := - #3 'after.block := -} - -STRINGS { s t } - -FUNCTION {output.nonnull} -{ 's := - output.state mid.sentence = - { ", " * write$ } - { output.state after.block = - { add.period$ write$ - newline$ - "\newblock " write$ - } - { output.state before.all = - 'write$ - { add.period$ " " * write$ } - if$ - } - if$ - mid.sentence 'output.state := - } - if$ - s -} - -FUNCTION {output} -{ duplicate$ empty$ - 'pop$ - 'output.nonnull - if$ -} - -FUNCTION {output.check} -{ 't := - duplicate$ empty$ - { pop$ "empty " t * " in " * cite$ * warning$ } - 'output.nonnull - if$ -} - -FUNCTION {output.bibitem} -{ newline$ - "\bibitem{" write$ - cite$ write$ - "}" write$ - newline$ - "" - before.all 'output.state := -} - -FUNCTION {fin.entry} -{ add.period$ - write$ - newline$ -} - -FUNCTION {new.block} -{ output.state before.all = - 'skip$ - { after.block 'output.state := } - if$ -} - -FUNCTION {new.sentence} -{ output.state after.block = - 'skip$ - { output.state before.all = - 'skip$ - { after.sentence 'output.state := } - if$ - } - if$ -} - -FUNCTION {not} -{ { #0 } - { #1 } - if$ -} - -FUNCTION {and} -{ 'skip$ - { pop$ #0 } - if$ -} - -FUNCTION {or} -{ { pop$ #1 } - 'skip$ - if$ -} - -FUNCTION {new.block.checka} -{ empty$ - 'skip$ - 'new.block - if$ -} - -FUNCTION {new.block.checkb} -{ empty$ - swap$ empty$ - and - 'skip$ - 'new.block - if$ -} - -FUNCTION {new.sentence.checka} -{ empty$ - 'skip$ - 'new.sentence - if$ -} - -FUNCTION {new.sentence.checkb} -{ empty$ - swap$ empty$ - and - 'skip$ - 'new.sentence - if$ -} - -FUNCTION {field.or.null} -{ duplicate$ empty$ - { pop$ "" } - 'skip$ - if$ -} - -FUNCTION {emphasize} -{ duplicate$ empty$ - { pop$ "" } - { "{\em " swap$ * "}" * } - if$ -} - -INTEGERS { nameptr namesleft numnames } - -FUNCTION {format.names} -{ 's := - #1 'nameptr := - s num.names$ 'numnames := - numnames 'namesleft := - { namesleft #0 > } - % Formerly { s nameptr "{f.~}{vv~}{ll}{, jj}" format.name$ 't := - { s nameptr "{ff }{vv }{ll}{, jj}" format.name$ 't := - nameptr #1 > - { namesleft #1 > - { ", " * t * } - { numnames #2 > - { "," * } - 'skip$ - if$ - t "others" = - { " et~al." * } - { " and " * t * } - if$ - } - if$ - } - 't - if$ - nameptr #1 + 'nameptr := - - namesleft #1 - 'namesleft := - } - while$ -} - -FUNCTION {format.authors} -{ author empty$ - { "" } - { author format.names } - if$ -} - -FUNCTION {format.editors} -{ editor empty$ - { "" } - { editor format.names - editor num.names$ #1 > - { ", editors" * } - { ", editor" * } - if$ - } - if$ -} - -FUNCTION {format.title} -{ title empty$ - { "" } - { title "t" change.case$ } - if$ -} - -FUNCTION {n.dashify} -{ 't := - "" - { t empty$ not } - { t #1 #1 substring$ "-" = - { t #1 #2 substring$ "--" = not - { "--" * - t #2 global.max$ substring$ 't := - } - { { t #1 #1 substring$ "-" = } - { "-" * - t #2 global.max$ substring$ 't := - } - while$ - } - if$ - } - { t #1 #1 substring$ * - t #2 global.max$ substring$ 't := - } - if$ - } - while$ -} - -FUNCTION {format.date} -{ year empty$ - { month empty$ - { "" } - { "there's a month but no year in " cite$ * warning$ - month - } - if$ - } - { month empty$ - 'year - { month " " * year * } - if$ - } - if$ -} - -FUNCTION {format.btitle} -{ title emphasize -} - -FUNCTION {tie.or.space.connect} -{ duplicate$ text.length$ #3 < - { "~" } - { " " } - if$ - swap$ * * -} - -FUNCTION {either.or.check} -{ empty$ - 'pop$ - { "can't use both " swap$ * " fields in " * cite$ * warning$ } - if$ -} - -FUNCTION {format.bvolume} -{ volume empty$ - { "" } - { "volume" volume tie.or.space.connect - series empty$ - 'skip$ - { " of " * series emphasize * } - if$ - "volume and number" number either.or.check - } - if$ -} - -FUNCTION {format.number.series} -{ volume empty$ - { number empty$ - { series field.or.null } - { output.state mid.sentence = - { "number" } - { "Number" } - if$ - number tie.or.space.connect - series empty$ - { "there's a number but no series in " cite$ * warning$ } - { " in " * series * } - if$ - } - if$ - } - { "" } - if$ -} - -FUNCTION {format.edition} -{ edition empty$ - { "" } - { output.state mid.sentence = - { edition "l" change.case$ " edition" * } - { edition "t" change.case$ " edition" * } - if$ - } - if$ -} - -INTEGERS { multiresult } - -FUNCTION {multi.page.check} -{ 't := - #0 'multiresult := - { multiresult not - t empty$ not - and - } - { t #1 #1 substring$ - duplicate$ "-" = - swap$ duplicate$ "," = - swap$ "+" = - or or - { #1 'multiresult := } - { t #2 global.max$ substring$ 't := } - if$ - } - while$ - multiresult -} - -FUNCTION {format.pages} -{ pages empty$ - { "" } - { pages multi.page.check - { "pages" pages n.dashify tie.or.space.connect } - { "page" pages tie.or.space.connect } - if$ - } - if$ -} - -FUNCTION {format.vol.num.pages} -{ volume field.or.null - number empty$ - 'skip$ - { "(" number * ")" * * - volume empty$ - { "there's a number but no volume in " cite$ * warning$ } - 'skip$ - if$ - } - if$ - pages empty$ - 'skip$ - { duplicate$ empty$ - { pop$ format.pages } - { ":" * pages n.dashify * } - if$ - } - if$ -} - -FUNCTION {format.chapter.pages} -{ chapter empty$ - 'format.pages - { type empty$ - { "chapter" } - { type "l" change.case$ } - if$ - chapter tie.or.space.connect - pages empty$ - 'skip$ - { ", " * format.pages * } - if$ - } - if$ -} - -FUNCTION {format.in.ed.booktitle} -{ booktitle empty$ - { "" } - { editor empty$ - { "In " booktitle emphasize * } - { "In " format.editors * ", " * booktitle emphasize * } - if$ - } - if$ -} - -FUNCTION {empty.misc.check} - -{ author empty$ title empty$ howpublished empty$ - month empty$ year empty$ note empty$ - and and and and and - key empty$ not and - { "all relevant fields are empty in " cite$ * warning$ } - 'skip$ - if$ -} - -FUNCTION {format.thesis.type} -{ type empty$ - 'skip$ - { pop$ - type "t" change.case$ - } - if$ -} - -FUNCTION {format.tr.number} -{ type empty$ - { "Technical Report" } - 'type - if$ - number empty$ - { "t" change.case$ } - { number tie.or.space.connect } - if$ -} - -FUNCTION {format.article.crossref} -{ key empty$ - { journal empty$ - { "need key or journal for " cite$ * " to crossref " * crossref * - warning$ - "" - } - { "In {\em " journal * "\/}" * } - if$ - } - { "In " key * } - if$ - " \cite{" * crossref * "}" * -} - -FUNCTION {format.crossref.editor} -{ editor #1 "{vv~}{ll}" format.name$ - editor num.names$ duplicate$ - #2 > - { pop$ " et~al." * } - { #2 < - 'skip$ - { editor #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = - { " et~al." * } - { " and " * editor #2 "{vv~}{ll}" format.name$ * } - if$ - } - if$ - } - if$ -} - -FUNCTION {format.book.crossref} -{ volume empty$ - { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ - "In " - } - { "Volume" volume tie.or.space.connect - " of " * - } - if$ - editor empty$ - editor field.or.null author field.or.null = - or - { key empty$ - { series empty$ - { "need editor, key, or series for " cite$ * " to crossref " * - crossref * warning$ - "" * - } - { "{\em " * series * "\/}" * } - if$ - } - { key * } - if$ - } - { format.crossref.editor * } - if$ - " \cite{" * crossref * "}" * -} - -FUNCTION {format.incoll.inproc.crossref} -{ editor empty$ - editor field.or.null author field.or.null = - or - { key empty$ - { booktitle empty$ - { "need editor, key, or booktitle for " cite$ * " to crossref " * - crossref * warning$ - "" - } - { "In {\em " booktitle * "\/}" * } - if$ - } - { "In " key * } - if$ - } - { "In " format.crossref.editor * } - if$ - " \cite{" * crossref * "}" * -} - -FUNCTION {article} -{ output.bibitem - format.authors "author" output.check - new.block - format.title "title" output.check - new.block - crossref missing$ - { journal emphasize "journal" output.check - format.vol.num.pages output - format.date "year" output.check - } - { format.article.crossref output.nonnull - format.pages output - } - if$ - new.block - note output - fin.entry -} - -FUNCTION {book} -{ output.bibitem - author empty$ - { format.editors "author and editor" output.check } - { format.authors output.nonnull - crossref missing$ - { "author and editor" editor either.or.check } - 'skip$ - if$ - } - if$ - new.block - format.btitle "title" output.check - crossref missing$ - { format.bvolume output - new.block - format.number.series output - new.sentence - publisher "publisher" output.check - address output - } - { new.block - format.book.crossref output.nonnull - } - if$ - format.edition output - format.date "year" output.check - new.block - note output - fin.entry -} - -FUNCTION {booklet} -{ output.bibitem - format.authors output - new.block - format.title "title" output.check - howpublished address new.block.checkb - howpublished output - address output - format.date output - new.block - note output - fin.entry -} - -FUNCTION {inbook} -{ output.bibitem - author empty$ - { format.editors "author and editor" output.check } - { format.authors output.nonnull - - crossref missing$ - { "author and editor" editor either.or.check } - 'skip$ - if$ - } - if$ - new.block - format.btitle "title" output.check - crossref missing$ - { format.bvolume output - format.chapter.pages "chapter and pages" output.check - new.block - format.number.series output - new.sentence - publisher "publisher" output.check - address output - } - { format.chapter.pages "chapter and pages" output.check - new.block - format.book.crossref output.nonnull - } - if$ - format.edition output - format.date "year" output.check - new.block - note output - fin.entry -} - -FUNCTION {incollection} -{ output.bibitem - format.authors "author" output.check - new.block - format.title "title" output.check - new.block - crossref missing$ - { format.in.ed.booktitle "booktitle" output.check - format.bvolume output - format.number.series output - format.chapter.pages output - new.sentence - publisher "publisher" output.check - address output - format.edition output - format.date "year" output.check - } - { format.incoll.inproc.crossref output.nonnull - format.chapter.pages output - } - if$ - new.block - note output - fin.entry -} - -FUNCTION {inproceedings} -{ output.bibitem - format.authors "author" output.check - new.block - format.title "title" output.check - new.block - crossref missing$ - { format.in.ed.booktitle "booktitle" output.check - format.bvolume output - format.number.series output - format.pages output - address empty$ - { organization publisher new.sentence.checkb - organization output - publisher output - format.date "year" output.check - } - { address output.nonnull - format.date "year" output.check - new.sentence - organization output - publisher output - } - if$ - } - { format.incoll.inproc.crossref output.nonnull - format.pages output - } - if$ - new.block - note output - fin.entry -} - -FUNCTION {conference} { inproceedings } - -FUNCTION {manual} -{ output.bibitem - author empty$ - { organization empty$ - 'skip$ - { organization output.nonnull - address output - } - if$ - } - { format.authors output.nonnull } - if$ - new.block - format.btitle "title" output.check - author empty$ - { organization empty$ - { address new.block.checka - address output - } - 'skip$ - if$ - } - { organization address new.block.checkb - organization output - address output - } - if$ - format.edition output - format.date output - new.block - note output - fin.entry -} - -FUNCTION {mastersthesis} -{ output.bibitem - format.authors "author" output.check - new.block - format.title "title" output.check - new.block - "Master's thesis" format.thesis.type output.nonnull - school "school" output.check - address output - format.date "year" output.check - new.block - note output - fin.entry -} - -FUNCTION {misc} -{ output.bibitem - format.authors output - title howpublished new.block.checkb - format.title output - howpublished new.block.checka - howpublished output - format.date output - new.block - note output - fin.entry - empty.misc.check -} - -FUNCTION {phdthesis} -{ output.bibitem - format.authors "author" output.check - new.block - format.btitle "title" output.check - new.block - "PhD thesis" format.thesis.type output.nonnull - school "school" output.check - address output - format.date "year" output.check - new.block - note output - fin.entry -} - -FUNCTION {proceedings} -{ output.bibitem - editor empty$ - { organization output } - { format.editors output.nonnull } - - if$ - new.block - format.btitle "title" output.check - format.bvolume output - format.number.series output - address empty$ - { editor empty$ - { publisher new.sentence.checka } - { organization publisher new.sentence.checkb - organization output - } - if$ - publisher output - format.date "year" output.check - } - { address output.nonnull - format.date "year" output.check - new.sentence - editor empty$ - 'skip$ - { organization output } - if$ - publisher output - } - if$ - new.block - note output - fin.entry -} - -FUNCTION {techreport} -{ output.bibitem - format.authors "author" output.check - new.block - format.title "title" output.check - new.block - format.tr.number output.nonnull - institution "institution" output.check - address output - format.date "year" output.check - new.block - note output - fin.entry -} - -FUNCTION {unpublished} -{ output.bibitem - format.authors "author" output.check - new.block - format.title "title" output.check - new.block - note "note" output.check - format.date output - fin.entry -} - -FUNCTION {default.type} { misc } - -MACRO {jan} {"Jan."} - -MACRO {feb} {"Feb."} - -MACRO {mar} {"Mar."} - -MACRO {apr} {"Apr."} - -MACRO {may} {"May"} - -MACRO {jun} {"June"} - -MACRO {jul} {"July"} - -MACRO {aug} {"Aug."} - -MACRO {sep} {"Sept."} - -MACRO {oct} {"Oct."} - -MACRO {nov} {"Nov."} - -MACRO {dec} {"Dec."} - -MACRO {acmcs} {"ACM Comput. Surv."} - -MACRO {acta} {"Acta Inf."} - -MACRO {cacm} {"Commun. ACM"} - -MACRO {ibmjrd} {"IBM J. Res. Dev."} - -MACRO {ibmsj} {"IBM Syst.~J."} - -MACRO {ieeese} {"IEEE Trans. Softw. Eng."} - -MACRO {ieeetc} {"IEEE Trans. Comput."} - -MACRO {ieeetcad} - {"IEEE Trans. Comput.-Aided Design Integrated Circuits"} - -MACRO {ipl} {"Inf. Process. Lett."} - -MACRO {jacm} {"J.~ACM"} - -MACRO {jcss} {"J.~Comput. Syst. Sci."} - -MACRO {scp} {"Sci. Comput. Programming"} - -MACRO {sicomp} {"SIAM J. Comput."} - -MACRO {tocs} {"ACM Trans. Comput. Syst."} - -MACRO {tods} {"ACM Trans. Database Syst."} - -MACRO {tog} {"ACM Trans. Gr."} - -MACRO {toms} {"ACM Trans. Math. Softw."} - -MACRO {toois} {"ACM Trans. Office Inf. Syst."} - -MACRO {toplas} {"ACM Trans. Prog. Lang. Syst."} - -MACRO {tcs} {"Theoretical Comput. Sci."} - -READ - -FUNCTION {sortify} -{ purify$ - "l" change.case$ -} - -INTEGERS { len } - -FUNCTION {chop.word} -{ 's := - 'len := - s #1 len substring$ = - { s len #1 + global.max$ substring$ } - 's - if$ -} - -FUNCTION {sort.format.names} -{ 's := - #1 'nameptr := - "" - s num.names$ 'numnames := - numnames 'namesleft := - { namesleft #0 > } - { nameptr #1 > - { " " * } - 'skip$ - if$ - s nameptr "{vv{ } }{ll{ }}{ f{ }}{ jj{ }}" format.name$ 't := - nameptr numnames = t "others" = and - { "et al" * } - { t sortify * } - if$ - nameptr #1 + 'nameptr := - namesleft #1 - 'namesleft := - } - while$ -} - -FUNCTION {sort.format.title} -{ 't := - "A " #2 - "An " #3 - "The " #4 t chop.word - chop.word - chop.word - sortify - #1 global.max$ substring$ -} - -FUNCTION {author.sort} -{ author empty$ - { key empty$ - { "to sort, need author or key in " cite$ * warning$ - "" - } - { key sortify } - if$ - } - { author sort.format.names } - if$ -} - -FUNCTION {author.editor.sort} -{ author empty$ - { editor empty$ - { key empty$ - { "to sort, need author, editor, or key in " cite$ * warning$ - "" - } - { key sortify } - if$ - } - { editor sort.format.names } - if$ - } - { author sort.format.names } - if$ -} - -FUNCTION {author.organization.sort} -{ author empty$ - - { organization empty$ - { key empty$ - { "to sort, need author, organization, or key in " cite$ * warning$ - "" - } - { key sortify } - if$ - } - { "The " #4 organization chop.word sortify } - if$ - } - { author sort.format.names } - if$ -} - -FUNCTION {editor.organization.sort} -{ editor empty$ - { organization empty$ - { key empty$ - { "to sort, need editor, organization, or key in " cite$ * warning$ - "" - } - { key sortify } - if$ - } - { "The " #4 organization chop.word sortify } - if$ - } - { editor sort.format.names } - if$ -} - -FUNCTION {presort} -{ type$ "book" = - type$ "inbook" = - or - 'author.editor.sort - { type$ "proceedings" = - 'editor.organization.sort - { type$ "manual" = - 'author.organization.sort - 'author.sort - if$ - } - if$ - } - if$ - " " - * - year field.or.null sortify - * - " " - * - title field.or.null - sort.format.title - * - #1 entry.max$ substring$ - 'sort.key$ := -} - -ITERATE {presort} - -SORT - -STRINGS { longest.label } - -INTEGERS { number.label longest.label.width } - -FUNCTION {initialize.longest.label} -{ "" 'longest.label := - #1 'number.label := - #0 'longest.label.width := -} - -FUNCTION {longest.label.pass} -{ number.label int.to.str$ 'label := - number.label #1 + 'number.label := - label width$ longest.label.width > - { label 'longest.label := - label width$ 'longest.label.width := - } - 'skip$ - if$ -} - -EXECUTE {initialize.longest.label} - -ITERATE {longest.label.pass} - -FUNCTION {begin.bib} -{ preamble$ empty$ - 'skip$ - { preamble$ write$ newline$ } - if$ - "\begin{thebibliography}{" longest.label * "}" * - "\itemsep=-1pt" * % Compact the entries a little. - write$ newline$ -} - -EXECUTE {begin.bib} - -EXECUTE {init.state.consts} - -ITERATE {call.type$} - -FUNCTION {end.bib} -{ newline$ - "\end{thebibliography}" write$ newline$ -} - -EXECUTE {end.bib} - -% end of file ieee.bst -% --------------------------------------------------------------- - - - diff --git a/images/.DS_Store b/images/.DS_Store new file mode 100644 index 0000000000..82f694ddcd Binary files /dev/null and b/images/.DS_Store differ diff --git a/images/3DSP.jpg b/images/3DSP.jpg deleted file mode 100644 index 0faf4cb6b8..0000000000 Binary files a/images/3DSP.jpg and /dev/null differ diff --git a/images/ArbalaezCVPR2014.jpg b/images/ArbalaezCVPR2014.jpg deleted file mode 100644 index a325723891..0000000000 Binary files a/images/ArbalaezCVPR2014.jpg and /dev/null differ diff --git a/images/B3DO.jpg b/images/B3DO.jpg deleted file mode 100644 index 6b40e4b62c..0000000000 Binary files a/images/B3DO.jpg and /dev/null differ diff --git a/images/BS_after.jpg b/images/BS_after.jpg deleted file mode 100644 index 3e3d220d9c..0000000000 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+ +

- Jon Barron + Pranamya Kulkarni +

+

+ I am a Pre-Doctoral Researcher at Google DeepMind India, working in the Machine Learning and Optimization (MLO) team. I work with Dr. Prateek Jain, Dr. Pradeep Shenoy, Dr. Karthikeyan Shanmugam, and Dr. Arun Suggala. My current research focuses on improving the training and inference efficiency of Large Language Models, as well as enabling label-free robotic pose estimation using causal representation learning. +

+

+ Previously, I completed an Interdisciplinary Dual Degree at IIT Bombay, earning a B.Tech (Honors) in Electrical Engineering and an M.Tech in Artificial Intelligence and Data Science. I was advised by Prof. Soumen Chakrabarti and Prof. Abir De, where I worked on Graph Neural Networks. I also completed a research internship at Adobe Research India.

- I'm a principal research scientist at Google DeepMind in San Francisco, where I lead a small team that mostly works on NeRF. - At Google I've worked on Glass, Lens Blur, HDR+, VR, Portrait Mode, Portrait Light, Maps, and Shopping. - I did my PhD at UC Berkeley, where I was advised by Jitendra Malik. - I've received the PAMI Young Researcher Award. + In my free time, I enjoy playing and watching cricket, playing chess, and gaming on PlayStation.

- Email  /  - CV  /  - Bio  /  - Scholar  /  - Twitter  /  - Github + + Email: pranamyakulkarni@gmail.com  /  + Resume  /  + Scholar +

- profile photo + + profile photo
+ + - - +
-

Research

-

- I'm interested in computer vision, deep learning, generative AI, and image processing. Most of my research is about inferring the physical world (shape, motion, color, light, etc) from images, usually with radiance fields. Some papers are highlighted. -

+
+

Projects
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- - 		- - Bolt3D: Generating 3D Scenes in Seconds - -
- Stanislaw Szymanowicz, - Jason Y. Zhang, - Pratul Srinivasan, - Ruiqi Gao, - Arthur Brussee, - Aleksander Holynski, - Ricardo Martin-Brualla, - Jonathan T. Barron, - Philipp Henzler -
- ICCV, 2025 -
- project page - / - arXiv -

-

- By training a latent diffusion model to directly output 3D Gaussians we enable fast (~6 seconds on a single GPU) feed-forward 3D scene generation. -

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- - 		- - EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis - - -
- Alexander Mai, - Peter Hedman, - George Kopanas, - Dor Verbin, - David Futschik, - Qiangeng Xu, - Falko Kuester, - Jonathan T. Barron, - Yinda Zhang -
- ICCV, 2025   (Oral Presentation) -
- project page - / - arXiv -

-

- Raytracing constant-density ellipsoids yields more accurate and flexible radiance fields than splatting Gaussians, and still runs in real-time. -

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- - 		- - CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models - - -
- Rundi Wu, - Ruiqi Gao, - Ben Poole, - Alex Trevithick, - Changxi Zheng, - Jonathan T. Barron, - Aleksander Holynski -
- CVPR, 2025   (Oral Presentation) -
- project page - / - arXiv -

-

- An approach for turning a video into a 4D radiance field that can be rendered in real-time. When combined with a text-to-video model, this enables text-to-4D. -

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- -
- - 		- - Generative Multiview Relighting for -3D Reconstruction under Extreme Illumination Variation - -
- Hadi Alzayer, - Philipp Henzler, - Jonathan T. Barron, - Jia-Bin Huang, - Pratul P. Srinivasan, - Dor Verbin -
- CVPR, 2025   (Highlight) -
- project page - / - arXiv -

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- Images taken under extreme illumination variation can be made consistent with diffusion, and this enables high-quality 3D reconstruction. -

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-
-
- -
- - 		- - SimVS: Simulating World Inconsistencies for Robust View Synthesis - -
- Alex Trevithick, - Roni Paiss, - Philipp Henzler, - Dor Verbin, - Rundi Wu, - Hadi Alzayer, - Ruiqi Gao, - Ben Poole, - Jonathan T. Barron, - Aleksander Holynski, - Ravi Ramamoorthi, - Pratul P. Srinivasan -
- CVPR, 2025 -
- project page - / - arXiv -

-

- Simulating the world with video models lets you make inconsistent captures consistent. -

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- -
- - 		- - A Power Transform - - -
- Jonathan T. Barron -
- arXiv, 2025 -
- tweet - / - arXiv -

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- A slight tweak to the Box-Cox power transform generalizes a variety of curves, losses, kernel functions, probability distributions, bump functions, and neural network activation functions. -

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- - 		- - CAT3D: Create Anything in 3D with Multi-View Diffusion Models - - -
- Ruiqi Gao*, - Aleksander Holynski*, - Philipp Henzler, - Arthur Brussee, - Ricardo Martin Brualla, - Pratul P. Srinivasan, - Jonathan T. Barron, - Ben Poole* - -
- NeurIPS, 2024   (Oral Presentation) -
- project page - / - arXiv -

-

- A single model built around diffusion and NeRF that does text-to-3D, image-to-3D, and few-view reconstruction, trains in 1 minute, and renders at 60FPS in a browser. -

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- - 		- - NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections - -
- - Dor Verbin, - Pratul Srinivasan, - Peter Hedman, - Benjamin Attal,
- Ben Mildenhall, - Richard Szeliski, - Jonathan T. Barron -
- SIGGRAPH Asia, 2024 -
- project page - / - arXiv -

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- Carefully casting reflection rays lets us synthesize photorealistic specularities in real-world scenes. -

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- -
- - 		- - Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering - -
- Benjamin Attal, - Dor Verbin, - Ben Mildenhall, - Peter Hedman,
- Jonathan T. Barron, - Matthew O'Toole, - Pratul P. Srinivasan -
- ECCV, 2024   (Oral Presentation) -
- project page - / - arXiv -

-

- A more physically-accurate inverse rendering system based on radiance caching for recovering geometry, materials, and lighting from RGB images of an object or scene. -

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-
- -
- - 		- - Nuvo: Neural UV Mapping for Unruly 3D Representations - -
- Pratul Srinivasan, - Stephan J. Garbin, - Dor Verbin, - Jonathan T. Barron, - Ben Mildenhall -
- ECCV, 2024 -
- project page - / - video - / - arXiv -

-

- Neural fields let you recover editable UV mappings for the challenging geometries produced by NeRF-like models. -

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- - 		- - Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis - - -
- Christian Reiser, - Stephan J. Garbin, - Pratul Srinivasan, - Dor Verbin, - Richard Szeliski, - Ben Mildenhall, - Jonathan T. Barron, - Peter Hedman*, - Andreas Geiger* -
- SIGGRAPH, 2024 -
- project page - / - video - / - arXiv -

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- Applying anti-aliasing to a discrete opacity grid lets you render a hard representation into a soft image, and this enables highly-detailed mesh recovery. -

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- - 		- - SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration - -
- Daniel Duckworth*, - Peter Hedman*, - Christian Reiser, - Peter Zhizhin, - Jean-François Thibert, - Mario Lučić, - Richard Szeliski, - Jonathan T. Barron -
- SIGGRAPH, 2024   (Honorable Mention) -
- project page - / - video - / - arXiv -

-

- Distilling a Zip-NeRF into a tiled set of MERFs lets you fly through radiance fields on laptops and smartphones at 60 FPS. -

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- -
- - 		- - Eclipse: Disambiguating Illumination and Materials using Unintended Shadows - -
- Dor Verbin, - Ben Mildenhall, - Peter Hedman,
- Jonathan T. Barron, - Todd Zickler, - Pratul Srinivasan -
- CVPR, 2024   (Oral Presentation) -
- project page - / - video - / - arXiv -

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- Shadows cast by unobserved occluders provide a high-frequency cue for recovering illumination and materials. -

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- - 		- - ReconFusion: 3D Reconstruction with Diffusion Priors - -
- Rundi Wu*, - Ben Mildenhall*, - Philipp Henzler, - Keunhong Park, - Ruiqi Gao, - Daniel Watson, - Pratul P. Srinivasan, - Dor Verbin, - Jonathan T. Barron, - Ben Poole, - Aleksander Holynski* -
- CVPR, 2024 -
- project page - / - arXiv -

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- Using a multi-image diffusion model as a regularizer lets you recover high-quality radiance fields from just a handful of images. -

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- - 		- - SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-Wild - -
- - Andreas Engelhardt, - Amit Raj, - Mark Boss, - Yunzhi Zhang, - Abhishek Kar, - Yuanzhen Li, - Deqing Sun, - Ricardo Martin Brualla, - Jonathan T. Barron, - Hendrik P.A. Lensch, - Varun Jampani -
- CVPR, 2024 -
- project page - / - video - / - arXiv -

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- A class-agnostic inverse rendering solution for turning in-the-wild images of an object into a relightable 3D asset. -

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- -
- - 		- - InterNeRF: Scaling Radiance Fields via Parameter Interpolation - -
- Clinton Wang, - Peter Hedman, - Polina Golland, - Jonathan T. Barron, - Daniel Duckworth -
- CVPR Neural Rendering Intelligence, 2024 -
- arXiv -

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- Parameter interpolation enables high-quality large-scale scene reconstruction and out-of-core training and rendering. -

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- - 		- - State of the Art on Diffusion Models for Visual Computing - - -
- Ryan Po, - Wang Yifan, - Vladislav Golyanik, - Kfir Aberman, - Jonathan T. Barron, - Amit H. Bermano, - Eric Ryan Chan, - Tali Dekel, - Aleksander Holynski, - Angjoo Kanazawa, - C. Karen Liu, - Lingjie Liu, - Ben Mildenhall, - Matthias Nießner, - Björn Ommer, - Christian Theobalt, - Peter Wonka, - Gordon Wetzstein -
- Eurographics State-of-the-Art Report, 2024 -
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- A survey of recent progress in diffusion models for images, videos, and 3D. -

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- - 		- - CamP: Camera Preconditioning for Neural Radiance Fields - -
- Keunhong Park, - Philipp Henzler, - Ben Mildenhall, - Jonathan T. Barron, - Ricardo Martin-Brualla -
- SIGGRAPH Asia, 2023 -
- project page - / - arXiv -

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- Preconditioning based on camera parameterization helps NeRF and camera extrinsics/intrinsics optimize better together. -

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- - 		- - Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields - -
- Jonathan T. Barron, - Ben Mildenhall, - Dor Verbin, - Pratul Srinivasan, - Peter Hedman -
- ICCV, 2023   (Oral Presentation, Best Paper Finalist) -
- project page - / - video - / - arXiv -

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- Combining mip-NeRF 360 and grid-based models like Instant NGP lets us reduce error rates by 8%–77% and accelerate training by 24x. -

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- - 		- - DreamBooth3D: Subject-Driven Text-to-3D Generation - -
- - Amit Raj, Srinivas Kaza, Ben Poole, Michael Niemeyer, Nataniel Ruiz, - Ben Mildenhall, Shiran Zada, Kfir Aberman, Michael Rubinstein, - Jonathan T. Barron, Yuanzhen Li, Varun Jampani -
- ICCV, 2023 -
- project page / - arXiv -

-

Combining DreamBooth (personalized text-to-image) and DreamFusion (text-to-3D) yields high-quality, subject-specific 3D assets with text-driven modifications

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- -
- - 		- - BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis - -
- Lior Yariv*, - Peter Hedman*, - Christian Reiser, - Dor Verbin,
- Pratul Srinivasan, - Richard Szeliski, - Jonathan T. Barron, - Ben Mildenhall -
- SIGGRAPH, 2023 -
- project page - / - video - / - arXiv -

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- We use SDFs to bake a NeRF-like model into a high quality mesh and do real-time view synthesis. -

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- -
- - 		- - MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes - -
- Christian Reiser, - Richard Szeliski, - Dor Verbin, - Pratul Srinivasan,
- Ben Mildenhall, - Andreas Geiger, - Jonathan T. Barron, - Peter Hedman -
- SIGGRAPH, 2023 -
- project page - / - video - / - arXiv -

-

- We use volumetric rendering with a sparse 3D feature grid and 2D feature planes to do real-time view synthesis. -

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- -
- - 		- - AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training - -
- Yifan Jiang, - Peter Hedman, - Ben Mildenhall, - Dejia Xu,
- Jonathan T. Barron, - Zhangyang Wang, - Tianfan Xue -
- CVPR, 2023 -
- project page - / - arXiv -

-

- Accounting for misalignment due to scene motion or calibration errors improves NeRF reconstruction quality. -

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- -
- - 		- - DreamFusion: Text-to-3D using 2D Diffusion - -
- Ben Poole, - Ajay Jain, - Jonathan T. Barron, - Ben Mildenhall -
- ICLR, 2023   (Oral Presentation, Outstanding Paper Award) -
- project page - / - arXiv - / - gallery -

-

- We optimize a NeRF from scratch using a pretrained text-to-image diffusion model to do text-to-3D generative modeling. -

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- -
- - 		- - Learning a Diffusion Prior for NeRFs - -
- Guandao Yang, - Abhijit Kundu, - Leonidas J. Guibas, - Jonathan T. Barron, - Ben Poole -
- ICLR Workshop, 2023 -

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- Training a diffusion model on grid-based NeRFs lets you (conditionally) sample NeRFs. -

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- -
- - 		- - MIRA: Mental Imagery for Robotic Affordances - -
- Lin Yen-Chen, - Pete Florence, - Andy Zeng, Jonathan T. Barron, - Yilun Du, - Wei-Chiu Ma, - Anthony Simeonov, - Alberto Rodriguez, - Phillip Isola -
- CoRL, 2022 -

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- NeRF lets us synthesize novel orthographic views that work well with pixel-wise algorithms for robotic manipulation. -

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+ MemCom project thumbnail - - SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image Collections + + + Compressing Many-Shots in In-Context Learning
- Mark Boss, - Andreas Engelhardt, - Abhishek Kar, - Yuanzhen Li, - Deqing Sun, - Jonathan T. Barron, - Hendrik P. A. Lensch, - Varun Jampani + Devvrit Khatri, Pranamya Kulkarni, Nilesh Gupta, Yerram Varun, Liqian Peng, Jay Yagnik, Praneeth Netrapalli, Cho-Jui Hsieh, Alec Go, Inderjit S Dhillon, Aditya Kusupati, Pratek Jain
- NeurIPS, 2022 + Preprint available on arXiv
- project page / - video / - arXiv -

+ arXiv

- A joint optimization framework for estimating shape, BRDF, camera pose, and illumination from in-the-wild image collections. + We introduce MemCom, a layer-wise compression method designed to improve the memory and computational efficiency of many-shot In-Context Learning (ICL). By compressing prompts into a compact key-value (KV) cache, MemCom significantly reduces inference costs while maintaining high performance across various classification tasks.

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- -
- - 		- - Polynomial Neural Fields for Subband Decomposition -
- Guandao Yang*, - Sagie Benaim*, - Varun Jampani, - Kyle Genova, - Jonathan T. Barron, - Thomas Funkhouser, - Bharath Hariharan, - Serge Belongie -
- NeurIPS, 2022 -

- Representing neural fields as a composition of manipulable and interpretable components lets you do things like reason about frequencies and scale. -

-
-
-
-
- -
- + +
+ ROPES project thumbnail - - Fast and High-Quality Image Denoising via Malleable Convolutions + + + ROPES: Robotic Pose Estimation via Score-Based Causal Representation Learning
- Yifan Jiang, - Bartlomiej Wronski, - Ben Mildenhall,
- Jonathan T. Barron, - Zhangyang Wang, - Tianfan Xue + Pranamya Kulkarni*, Puranjay Datta*, Emre Acartürk, Burak Varıcı, Karthikeyan Shanmugam, Ali Tajer
- ECCV, 2022 + Accepted to the Embodied World Models for Decision Making Workshop at NeurIPS 2025
- project page - / - arXiv -

+ arXiv

- We denoise images efficiently by predicting spatially-varying kernels at low resolution and using a fast fused op to jointly upsample and apply these kernels at full resolution. + We propose ROPES, an unsupervised framework for robotic pose estimation that recovers joint angles from raw images using score-based Causal Representation Learning. By leveraging interventional data distributions, ROPES disentangles controllable latent variables without requiring any explicit pose labels.

-
-
- -
- - 		- - NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields - -
- Lin Yen-Chen, - Pete Florence, - Jonathan T. Barron,
- Tsung-Yi Lin, - Alberto Rodriguez, - Phillip Isola -
- ICRA, 2022 -
- project page / - arXiv / - video / - code / - colab -

-

NeRF works better than RGB-D cameras or multi-view stereo when learning object descriptors.

-
-
-
- -
- - 		- - Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields - -
- Dor Verbin, - Peter Hedman, - Ben Mildenhall,
- Todd Zickler, - Jonathan T. Barron, - Pratul Srinivasan -
- CVPR, 2022   (Oral Presentation, Best Student Paper Honorable Mention) -
- project page - / - arXiv - / - video -

-

Explicitly modeling reflections in NeRF produces realistic shiny surfaces and accurate surface normals, and lets you edit materials.

-
-
-
- -
- - 		- - Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields - -
- Jonathan T. Barron, - Ben Mildenhall, - Dor Verbin, - Pratul Srinivasan, - Peter Hedman -
- CVPR, 2022   (Oral Presentation) -
- project page - / - arXiv - / - video -

-

mip-NeRF can be extended to produce realistic results on unbounded scenes.

-
-
-
- -
- + +
+ M3CoL project thumbnail - - NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images - + + M3CoL: Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal Classification
- Ben Mildenhall, - Peter Hedman, - Ricardo Martin-Brualla,
- Pratul Srinivasan, - Jonathan T. Barron + Raja Kumar*, Raghav Singhal*, Pranamya Kulkarni, Deval Mehta, Kshitij Jadhav
- CVPR, 2022   (Oral Presentation) + TMLR
- project page - / - arXiv - / - video -

+ project page / + code / + arXiv

- Properly training NeRF on raw camera data enables HDR view synthesis and bokeh, and outperforms multi-image denoising.

-
-
-
- -
- - 		- - RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs - -
- Michael Niemeyer, - Jonathan T. Barron, - Ben Mildenhall,
- Mehdi S. M. Sajjadi, - Andreas Geiger, - Noha Radwan -
- CVPR, 2022   (Oral Presentation) -
- project page - / - arXiv - / - video -

-

Regularizing unseen views during optimization enables view synthesis from as few as 3 input images.

-
-
-
- -
- - 		- - Block-NeRF: Scalable Large Scene Neural View Synthesis - -
- Matthew Tancik, - Vincent Casser, - Xinchen Yan, - Sabeek Pradhan,
- Ben Mildenhall, - Pratul Srinivasan, - Jonathan T. Barron, - Henrik Kretzschmar -
- CVPR, 2022   (Oral Presentation) -
- project page - / - arXiv - / - video -

-

We can do city-scale reconstruction by training multiple NeRFs with millions of images.

-
-
-
- -
- - 		- - HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video - -
- Chung-Yi Weng, - Brian Curless, - Pratul Srinivasan,
- Jonathan T. Barron, - Ira Kemelmacher-Shlizerman -
- CVPR, 2022   (Oral Presentation) -
- project page - / - arXiv - / - video -

-

Combining NeRF with pose estimation lets you use a monocular video to do free-viewpoint rendering of a human.

+ We introduce a multimodal mixup-based contrastive learning framework that effectively captures shared relations across modalities, enabling robust multimodal representation learning. +

-
-
- -
- - 		- - Urban Radiance Fields - -
- Konstantinos Rematas, - Andrew Liu, - Pratul P. Srinivasan, - Jonathan T. Barron,
- Andrea Tagliasacchi, - Tom Funkhouser, - Vittorio Ferrari -
- CVPR, 2022 -
- project page - / - arXiv - / - video -

-

- Incorporating lidar and explicitly modeling the sky lets you reconstruct urban environments.

-
-
-
-
- -
- - 		- - Dense Depth Priors for Neural Radiance Fields from Sparse Input Views - -
- Barbara Roessle, - Jonathan T. Barron, - Ben Mildenhall, - Pratul Srinivasan, - Matthias Nießner -
- CVPR, 2022 -
- arXiv - / - video -

-

- Dense depth completion techniques applied to freely-available sparse stereo data can improve NeRF reconstructions in low-data regimes. -

-
-
-
- -
- - 		- - Zero-Shot Text-Guided Object Generation with Dream Fields - -
- Ajay Jain, - Ben Mildenhall, - Jonathan T. Barron, - Pieter Abbeel, - Ben Poole -
- CVPR, 2022 -
- project page - / - arXiv - / - video -

-

Supervising the CLIP embeddings of NeRF renderings lets you to generate 3D objects from text prompts.

-
-
-
-
- -
- + +
+ IndiBias project thumbnail - - Advances in Neural Rendering - + + IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context
- Ayush Tewari, - Justus Thies, - Ben Mildenhall, - Pratul Srinivasan, - Edgar Tretschk, - Yifan Wang, - Christoph Lassner, - Vincent Sitzmann, - Ricardo Martin-Brualla, - Stephen Lombardi, - Tomas Simon, - Christian Theobalt, - Matthias Niessner, - Jonathan T. Barron, - Gordon Wetzstein, - Michael Zollhoefer, - Vladislav Golyanik + Nihar Ranjan Sahoo, Pranamya Kulkarni, Narjis Asad, Arif Ahmad, Tanu Goyal, Aparna Garimella, Pushpak Bhattacharyya
- State of the Art Report at EUROGRAPHICS, 2022 + NAACL 2024
-

+ Paper

- A survey of recent progress in neural rendering. + We introduce IndiBias, a comprehensive benchmarking dataset designed to evaluate social biases in Large Language Models within the unique socio-cultural nuances of the Indian context. The dataset is built by adapting existing resources and leveraging LLMs to cover diverse biases like gender, religion, caste, and more.

-
-
-
- -
- + +
+ Image Description project thumbnail - - Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition + + + Image Description Generation with Varying Levels of Detail
- - Mark Boss, - Varun Jampani, - Raphael Braun,
- Ce Liu, - Jonathan T. Barron, - Hendrik P. A. Lensch + Pranamya Kulkarni*, Akshay Iyer*, Kanad Pardeshi*, Nikunj Goyal*, Apoorv Saxena, Praneetha Vaddamanu, Abhilasha Sancheti, Aparna Garimella, Vishwa Vinay
- NeurIPS, 2021 + U.S. Patent Application Filed
- project page / - video / - arXiv -

+ Patent

- Replacing a costly illumination integral with a simple network query enables more accurate novel view-synthesis and relighting compared to NeRD. + We present a system for generating image descriptions with controllable levels of detail, catering to diverse user needs from low-level summaries to high-granularity captions. Our approach introduces a novel scoring mechanism and utilizes architectures like DetailBERT and DenseDetail to allow users to explicitly specify the amount of information required in the output.

-
-
- -
- - 		- - HyperNeRF: A Higher-Dimensional Representation -for Topologically Varying Neural Radiance Fields - -
- Keunhong Park, - Utkarsh Sinha, - Peter Hedman, - Jonathan T. Barron,
- Sofien Bouaziz, - Dan B Goldman, - Ricardo Martin-Brualla, - Steven M. Seitz -
- SIGGRAPH Asia, 2021 -
- project page - / - arXiv -

-

Applying ideas from level set methods to NeRF lets you represent scenes that deform and change shape.

-
-
-
-
- -
- - 		- - NeRFactor: Neural Factorization of Shape and Reflectance
- Under an Unknown Illumination - -
- Xiuming Zhang, - Pratul Srinivasan, - Boyang Deng,
- Paul Debevec, - William T. Freeman, - Jonathan T. Barron -
- SIGGRAPH Asia, 2021 -
- project page - / - arXiv - / - video -

-

By placing priors on illumination and materials, we can recover NeRF-like models of the intrinsics of a scene from a single multi-image capture.

-
-
-
- -
- - 		- - Scalable Font Reconstruction with Dual Latent Manifolds - -
- Nikita Srivatsan, - Si Wu, - Jonathan T. Barron, - Taylor Berg-Kirkpatrick -
- EMNLP, 2021 -
-

-

VAEs can be used to disentangle a font's style from its content, and to generalize to characters that were never observed during training.

-
-
-
- -
- - 		- - Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields - -
- Jonathan T. Barron, - Ben Mildenhall, - Matthew Tancik,
- Peter Hedman, - Ricardo Martin-Brualla, - Pratul Srinivasan -
- ICCV, 2021   (Oral Presentation, Best Paper Honorable Mention) -
- project page - / - arXiv - / - video - / - code -

-

NeRF is aliased, but we can anti-alias it by casting cones and prefiltering the positional encoding function.

-
-
-
- -
- - 		- - Baking Neural Radiance Fields for Real-Time View Synthesis - -
- Peter Hedman, - Pratul Srinivasan, - Ben Mildenhall, - Jonathan T. Barron, - Paul Debevec -
- ICCV, 2021   (Oral Presentation) -
- project page - / - arXiv - / - video - / - demo -

-

Baking a trained NeRF into a sparse voxel grid of colors and features lets you render it in real-time in your browser.

-
-
-
- -
- - 		- - Nerfies: Deformable Neural Radiance Fields - -
- - Keunhong Park, - Utkarsh Sinha, - Jonathan T. Barron,
- Sofien Bouaziz, - Dan B Goldman, - Steven M. Seitz, - Ricardo-Martin Brualla -
- ICCV, 2021   (Oral Presentation) -
- project page / - arXiv / - video -

-

Building deformation fields into NeRF lets you capture non-rigid subjects, like people. -

-
-
-
-
- -
- - 		- - Cross-Camera Convolutional Color Constancy - -
- Mahmoud Afifi, - Jonathan T. Barron, - Chloe LeGendre, - Yun-Ta Tsai, - Francois Bleibel -
- ICCV, 2021   (Oral Presentation) + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

-

-

- With some extra (unlabeled) test-set images, you can build a hypernetwork that calibrates itself at test time to previously-unseen cameras. +

+ Website template by Jon Barron.

-
-
-
- -
- - 		- - Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image - -
- Shumian Xin, - Neal Wadhwa, - Tianfan Xue, - Jonathan T. Barron,
- Pratul Srinivasan, - Jiawen Chen, - Ioannis Gkioulekas, - Rahul Garg -
- ICCV, 2021   (Oral Presentation) -
- project page / - code -
-

-

- Multiplane images can be used to simultaneously deblur dual-pixel images, despite variable defocus due to depth variation in the scene. -

-
-
-
- -
- - 		- - NeRD: Neural Reflectance Decomposition from Image Collections - -
- - Mark Boss, - Raphael Braun, - Varun Jampani, - Jonathan T. Barron, - Ce Liu, - Hendrik P. A. Lensch -
- ICCV, 2021 -
- project page / - video / - code / - arXiv -

-

- A NeRF-like model that can decompose (and mesh) objects with non-Lambertian reflectances, complex geometry, and unknown illumination. -

-
-
-
-
- -
- - 		- - How to Train Neural Networks for Flare Removal - -
- Yicheng Wu, - Qiurui He, - Tianfan Xue, - Rahul Garg,
- Jiawen Chen, - Ashok Veeraraghavan, - Jonathan T. Barron -
- ICCV, 2021 -
- project page / - arXiv -

-

- Simulating the optics of a camera's lens lets you train a model that removes lens flare from a single image. -

-
-
-
- -
- - 		- - iNeRF: Inverting Neural Radiance Fields for Pose Estimation - -
- Lin Yen-Chen, - Pete Florence, - Jonathan T. Barron,
- Alberto Rodriguez, - Phillip Isola, - Tsung-Yi Lin -
- IROS, 2021 -
- project page / - arXiv / - video -

-

Given an image of an object and a NeRF of that object, you can estimate that object's pose. -

-
-
-
- -
- - 		- - IBRNet: Learning Multi-View Image-Based Rendering - -
- Qianqian Wang, - Zhicheng Wang, - Kyle Genova, - Pratul Srinivasan, - Howard Zhou,
- Jonathan T. Barron, - Ricardo Martin-Brualla, - Noah Snavely, - Thomas Funkhouser -
- CVPR, 2021 -
- project page / - code / - arXiv -

-

By learning how to pay attention to input images at render time, - we can amortize inference for view synthesis and reduce error rates by 15%.

-
-
-
- -
- - 		- - NeRV: Neural Reflection and Visibility Fields for Relighting and View Synthesis - -
- Pratul Srinivasan, - Boyang Deng, - Xiuming Zhang,
- Matthew Tancik, - Ben Mildenhall, - Jonathan T. Barron -
- CVPR, 2021 -
- project page / - video / - arXiv -

-

Using neural approximations of expensive visibility integrals lets you recover relightable NeRF-like models.

-
-
-
- -
- - 		- - Learned Initializations for Optimizing Coordinate-Based Neural Representations - -
- Matthew Tancik*, - Ben Mildenhall*, - Terrance Wang, - Divi Schmidt,
- Pratul Srinivasan, - Jonathan T. Barron, - Ren Ng -
- CVPR, 2021   (Oral Presentation) -
- project page / - video / - arXiv -

-

Using meta-learning to find weight initializations for coordinate-based MLPs allows them to converge faster and generalize better.

-
-
-
- -
- - 		- - NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections - -
- Ricardo Martin-Brualla*, - Noha Radwan*, - Mehdi S. M. Sajjadi*,
- Jonathan T. Barron, - Alexey Dosovitskiy, - Daniel Duckworth -
- CVPR, 2021   (Oral Presentation) -
- project page / - arXiv / - video -

-

Letting NeRF reason about occluders and appearance variation produces photorealistic view synthesis using only unstructured internet photos.

-
-
-
-
- -
- - 		- - Learned Dual-View Reflection Removal - -
- Simon Niklaus, - Xuaner (Cecilia) Zhang, - Jonathan T. Barron,
- Neal Wadhwa, - Rahul Garg, - Feng Liu, - Tianfan Xue -
- WACV, 2021 -
- project page / - arXiv -

-

- Reflections and the things behind them often exhibit parallax, and this lets you remove reflections from stereo pairs. -

-
-
-
- -
- - 		- - Neural Light Transport for Relighting and View Synthesis - -
- Xiuming Zhang, - Sean Fanello, - Yun-Ta Tsai, - Tiancheng Sun, - Tianfan Xue, - Rohit Pandey, - Sergio Orts-Escolano, - Philip Davidson, - Christoph Rhemann, - Paul Debevec, - Jonathan T. Barron, - Ravi Ramamoorthi, - William T. Freeman -
- ACM TOG, 2021 -
- project page / - arXiv / - video -

-

Embedding a convnet within a predefined texture atlas enables simultaneous view synthesis and relighting.

-
-
-
-
- -
- - 		- - Light Stage Super-Resolution: Continuous High-Frequency Relighting - -
- Tiancheng Sun, - Zexiang Xu - Xiuming Zhang, - Sean Fanello, - Christoph Rhemann,
- Paul Debevec, - Yun-Ta Tsai, - Jonathan T. Barron, - Ravi Ramamoorthi -
- SIGGRAPH Asia, 2020 -
- project page / - arXiv -

-

- Scans for light stages are inherently aliased, but we can use learning to super-resolve them. -

-
-
-
-
- -
- - 		- - Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains - -
- Matthew Tancik*, - Pratul Srinivasan*, - Ben Mildenhall*, - Sara Fridovich-Keil,
- Nithin Raghavan, - Utkarsh Singhal, - Ravi Ramamoorthi, - Jonathan T. Barron, - Ren Ng -
- NeurIPS, 2020   (Spotlight) -
- project page / - video: 3 min, 10 min / - arXiv / - code -

-

Composing neural networks with a simple Fourier feature mapping allows them to learn detailed high-frequency functions.

-
-
-
-
- -
- - 		- - A Generalization of Otsu's Method and Minimum Error Thresholding - -
- Jonathan T. Barron -
- ECCV, 2020   (Spotlight) -
- code / - video / - bibtex -
-

-

- A simple and fast Bayesian algorithm that can be written in ~10 lines of code outperforms or matches giant CNNs on image binarization, and unifies three classic thresholding algorithms. -

-
-
-
-
- -
- - 		- - What Matters in Unsupervised Optical Flow - -
- Rico Jonschkowski, - Austin Stone, - Jonathan T. Barron,
- Ariel Gordon, - Kurt Konolige, - Anelia Angelova -
- ECCV, 2020   (Oral Presentation) -
- code -
-

-

- Extensive experimentation yields a simple optical flow technique that is trained on only unlabeled videos, but still works as well as supervised techniques. -

-
-
-
- -
- - 		- - NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis - -
- Ben Mildenhall*, - Pratul Srinivasan*, - Matthew Tancik*,
- Jonathan T. Barron, - Ravi Ramamoorthi, - Ren Ng -
- ECCV, 2020   (Oral Presentation, Best Paper Honorable Mention, CACM Research Highlight) -
- project page - / - arXiv - / - talk video - / - supp video - / - code - / - CACM (foreward) -

-

- Training a tiny non-convolutional neural network to reproduce a scene using volume rendering achieves photorealistic view synthesis.

-
-
-
-
- -
- - 		- - Portrait Shadow Manipulation - -
- Xuaner (Cecilia) Zhang, - Jonathan T. Barron, - Yun-Ta Tsai,
- Rohit Pandey, - Xiuming Zhang, - Ren Ng, - David E. Jacobs -
- SIGGRAPH, 2020 -
- project page / - video -

-

Networks can be trained to remove shadows cast on human faces and to soften harsh lighting.

-
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-
-
- -
- - 		- - Learning to Autofocus - -
- Charles Herrmann, - Richard Strong Bowen, - Neal Wadhwa,
- Rahul Garg, - Qiurui He, - Jonathan T. Barron, - Ramin Zabih -
- CVPR, 2020 -
- project page - / - arXiv -

-

Machine learning can be used to train cameras to autofocus (which is not the same problem as "depth from defocus").

-
-
-
- -
- - 		- - Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination - -
- Pratul Srinivasan*, - Ben Mildenhall*, - Matthew Tancik,
- Jonathan T. Barron, - Richard Tucker, - Noah Snavely -
- CVPR, 2020 -
- project page - / - code - / - arXiv - / - video -

-

We predict a volume from an input stereo pair that can be used to calculate incident lighting at any 3D point within a scene.

-
-
-
-
- -
- - 		- - Sky Optimization: Semantically Aware Image Processing of Skies in Low-Light Photography - -
- Orly Liba, - Longqi Cai, - Yun-Ta Tsai, - Elad Eban, - Yair Movshovitz-Attias,
- Yael Pritch, - Huizhong Chen, - Jonathan T. Barron -
- NTIRE CVPRW, 2020 -
- project page -

-

If you want to photograph the sky, it helps to know where the sky is.

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-
-
- -
- - 		- - Handheld Mobile Photography in Very Low Light - -
- Orly Liba, - Kiran Murthy, - Yun-Ta Tsai, - Timothy Brooks, - Tianfan Xue, - Nikhil Karnad, - Qiurui He, - Jonathan T. Barron, - Dillon Sharlet, - Ryan Geiss, - Samuel W. Hasinoff, - Yael Pritch, - Marc Levoy -
- SIGGRAPH Asia, 2019 -
- project page -
-

-

By rethinking metering, white balance, and tone mapping, we can take pictures in places too dark for humans to see clearly.

-
-
-
- -
- - 		- - A Deep Factorization of Style and Structure in Fonts - -
- Nikita Srivatsan, - Jonathan T. Barron, - Dan Klein, - Taylor Berg-Kirkpatrick -
- EMNLP, 2019   (Oral Presentation) -
-

-

Variational auto-encoders can be used to disentangle a characters style from its content.

-
-
-
- -
- - 		- - Learning Single Camera Depth Estimation using Dual-Pixels - -
- Rahul Garg, - Neal Wadhwa, - Sameer Ansari, - Jonathan T. Barron -
- ICCV, 2019   (Oral Presentation) -
- code / - bibtex -

-

Considering the optics of dual-pixel image sensors improves monocular depth estimation techniques.

-
-
-
- -
- - 		- - Single Image Portrait Relighting - -
- Tiancheng Sun, - Jonathan T. Barron, - Yun-Ta Tsai, - Zexiang Xu, Xueming Yu, - Graham Fyffe, Christoph Rhemann, Jay Busch, - Paul Debevec, - Ravi Ramamoorthi -
- SIGGRAPH, 2019 -
- project page / - arxiv / - video / - press / - bibtex -
-

-

Training a neural network on light stage scans and environment maps produces an effective relighting method.

-
-
-
- -
- - 		- - A General and Adaptive Robust Loss Function - -
- Jonathan T. Barron -
- CVPR, 2019   (Oral Presentation, Best Paper Award Finalist) -
- arxiv / - supplement / - video / - talk / - slides / - code: TF, JAX, pytorch / - reviews / - bibtex -

-

A single robust loss function is a superset of many other common robust loss functions, and allows training to automatically adapt the robustness of its own loss.

-
-
-
- -
- - 		- - Pushing the Boundaries of View Extrapolation with Multiplane Images - -
- Pratul P. Srinivasan, Richard Tucker, - Jonathan T. Barron, - Ravi Ramamoorthi, - Ren Ng, - Noah Snavely -
- CVPR, 2019   (Oral Presentation, Best Paper Award Finalist) -
- supplement / - video / - bibtex -

-

View extrapolation with multiplane images works better if you reason about disocclusions and disparity sampling frequencies.

-
-
-
- -
- - 		- - Unprocessing Images for Learned Raw Denoising - -
- Tim Brooks, - Ben Mildenhall, - Tianfan Xue, - Jiawen Chen, - Dillon Sharlet, - Jonathan T. Barron -
- CVPR, 2019   (Oral Presentation) -
- arxiv / - project page / - code / - bibtex -

-

We can learn a better denoising model by processing and unprocessing images the same way a camera does.

-
-
-
- -
- - 		- - Learning to Synthesize Motion Blur - -
- Tim Brooks, - Jonathan T. Barron -
- CVPR, 2019   (Oral Presentation) -
- arxiv / - supplement / - project page / - video / - code / - bibtex -

-

Frame interpolation techniques can be used to train a network that directly synthesizes linear blur kernels.

-
-
-
- -
- - 		- - Stereoscopic Dark Flash for Low-light Photography - -
- Jian Wang, - Tianfan Xue, - Jonathan T. Barron, - Jiawen Chen -
- ICCP, 2019 -
-

-

- By making one camera in a stereo pair hyperspectral we can multiplex dark flash pairs in space instead of time. -

-
-
-
- -
- - 		- - Depth from Motion for Smartphone AR - -
- Julien Valentin, - Adarsh Kowdle, - Jonathan T. Barron, Neal Wadhwa, and others -
- SIGGRAPH Asia, 2018 -
- planar filter toy code / - bibtex -

-

Depth cues from camera motion allow for real-time occlusion effects in augmented reality applications.

-
-
-
- -
- - 		- - Synthetic Depth-of-Field with a Single-Camera Mobile Phone - -
- Neal Wadhwa, - Rahul Garg, - David E. Jacobs, Bryan E. Feldman, Nori Kanazawa, Robert Carroll, - Yair Movshovitz-Attias, - Jonathan T. Barron, Yael Pritch, - Marc Levoy -
- SIGGRAPH, 2018 -
- arxiv / - blog post / - bibtex -

-

Dual pixel cameras and semantic segmentation algorithms can be used for shallow depth of field effects.

-

This system is the basis for "Portrait Mode" on the Google Pixel 2 smartphones

-
-
-
- -
- - 		- - Aperture Supervision for Monocular Depth Estimation - -
- Pratul P. Srinivasan, - Rahul Garg, - Neal Wadhwa, - Ren Ng, - Jonathan T. Barron -
- CVPR, 2018 -
- code / - bibtex -

-

Varying a camera's aperture provides a supervisory signal that can teach a neural network to do monocular depth estimation.

-
-
-
- -
- - 		- - Burst Denoising with Kernel Prediction Networks - -
- Ben Mildenhall, - Jonathan T. Barron, - Jiawen Chen, - Dillon Sharlet, - Ren Ng, Robert Carroll -
- CVPR, 2018   (Spotlight) -
- supplement / - code / - bibtex -

-

We train a network to predict linear kernels that denoise noisy bursts from cellphone cameras.

-
-
-
- -
- - 		- - A Hardware-Friendly Bilateral Solver for Real-Time Virtual Reality Video - -
- Amrita Mazumdar, Armin Alaghi, Jonathan T. Barron, David Gallup, Luis Ceze, Mark Oskin, Steven M. Seitz -
- High-Performance Graphics (HPG), 2017 -
- project page -

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A reformulation of the bilateral solver can be implemented efficiently on GPUs and FPGAs.

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- - 		- - Deep Bilateral Learning for Real-Time Image Enhancement - -
- Michaël Gharbi, Jiawen Chen, Jonathan T. Barron, Samuel W. Hasinoff, Frédo Durand -
- SIGGRAPH, 2017 -
- project page / - video / - bibtex / - press -

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By training a deep network in bilateral space we can learn a model for high-resolution and real-time image enhancement.

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- - 		- - Fast Fourier Color Constancy - -
- Jonathan T. Barron, - Yun-Ta Tsai, -
- CVPR, 2017 -
- video / - bibtex / - code / - output / - blog post / - press -

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Color space can be aliased, allowing white balance models to be learned and evaluated in the frequency domain. This improves accuracy by 13-20% and speed by 250-3000x.

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This technology is used by Google Pixel, Google Photos, and Google Maps.

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- - 		- - Jump: Virtual Reality Video - -
- Robert Anderson, David Gallup, Jonathan T. Barron, Janne Kontkanen, Noah Snavely, Carlos Hernández, Sameer Agarwal, Steven M Seitz -
- SIGGRAPH Asia, 2016 -
- supplement / - video / - bibtex / - blog post -

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Using computer vision and a ring of cameras, we can make video for virtual reality headsets that is both stereo and 360°.

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This technology is used by Jump.

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- - 		- - Burst Photography for High Dynamic Range and Low-Light Imaging on Mobile Cameras - -
- Samuel W. Hasinoff, Dillon Sharlet, Ryan Geiss, Andrew Adams, Jonathan T. Barron, Florian Kainz, Jiawen Chen, Marc Levoy -
- SIGGRAPH Asia, 2016 -
- project page / - supplement / - bibtex -

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Mobile phones can take beautiful photographs in low-light or high dynamic range environments by aligning and merging a burst of images.

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This technology is used by the Nexus HDR+ feature.

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- - 		- - The Fast Bilateral Solver - -
- Jonathan T. Barron, - Ben Poole -
- ECCV, 2016   (Oral Presentation, Best Paper Honorable Mention) -
- arXiv / - bibtex / - video (they messed up my slides, use →) / - keynote (or PDF) / - code / - depth super-res results / - reviews -

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Our solver smooths things better than other filters and faster than other optimization algorithms, and you can backprop through it.

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- - 		- - Geometric Calibration for Mobile, Stereo, Autofocus Cameras - -
- Stephen DiVerdi, - Jonathan T. Barron -
- WACV, 2016 -
- bibtex -

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Standard techniques for stereo calibration don't work for cheap mobile cameras.

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- - 		- - Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform - -
- CVPR, 2016 -
- Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, Alan L. Yuille -
- bibtex / - project page / - code -

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By integrating an edge-aware filter into a convolutional neural network we can learn an edge-detector while improving semantic segmentation.

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- - 		- - Convolutional Color Constancy - -
- Jonathan T. Barron -
- ICCV, 2015 -
- supplement / bibtex / video (or mp4) -

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By framing white balance as a chroma localization task we can discriminatively learn a color constancy model that beats the state-of-the-art by 40%.

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- - - - Scene Intrinsics and Depth from a Single Image - -
- Evan Shelhamer, Jonathan T. Barron, Trevor Darrell -
- ICCV Workshop, 2015 -
- bibtex -

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The monocular depth estimates produced by fully convolutional networks can be used to inform intrinsic image estimation.

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- - 		- - Fast Bilateral-Space Stereo for Synthetic Defocus - -
- Jonathan T. Barron, Andrew Adams, YiChang Shih, Carlos Hernández -
- CVPR, 2015   (Oral Presentation) -
- abstract / - supplement / - bibtex / - talk / - keynote (or PDF) -

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By embedding a stereo optimization problem in "bilateral-space" we can very quickly solve for an edge-aware depth map, letting us render beautiful depth-of-field effects.

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This technology is used by the Google Camera "Lens Blur" feature.

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- PontTuset - - - Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation - -
- Jordi Pont-Tuset, Pablo Arbeláez, Jonathan T. Barron, Ferran Marqués, Jitendra Malik -
- TPAMI, 2017 -
- project page / - bibtex / - fast eigenvector code -

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We produce state-of-the-art contours, regions and object candidates, and we compute normalized-cuts eigenvectors 20× faster.

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This paper subsumes our CVPR 2014 paper.

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- - Shape, Illumination, and Reflectance from Shading - -
- Jonathan T. Barron, Jitendra Malik -
- TPAMI, 2015 -
- bibtex / keynote (or powerpoint, PDF) / video / code & data / kudos -

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- We present SIRFS, which can estimate shape, chromatic illumination, reflectance, and shading from a single image of an masked object. -

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- This paper subsumes our CVPR 2011, CVPR 2012, and ECCV 2012 papers. -

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- ArbalaezCVPR2014 - - - Multiscale Combinatorial Grouping - -
- Pablo Arbeláez, Jordi Pont-Tuset, Jonathan T. Barron, Ferran Marqués, Jitendra Malik -
- CVPR, 2014 -
- project page / - bibtex -

This paper is subsumed by our journal paper.

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- - 		- - Volumetric Semantic Segmentation using Pyramid Context Features - -
- Jonathan T. Barron, Pablo Arbeláez, Soile V. E. Keränen, Mark D. Biggin, -
David W. Knowles, Jitendra Malik -
- ICCV, 2013 -
- supplement / - poster / - bibtex / video 1 (or mp4) / video 2 (or mp4) / code & data -

- We present a technique for efficient per-voxel linear classification, which enables accurate and fast semantic segmentation of volumetric Drosophila imagery. -

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- 3DSP - - - 3D Self-Portraits - -
- Hao Li, Etienne Vouga, Anton Gudym, Linjie Luo, Jonathan T. Barron, Gleb Gusev -
- SIGGRAPH Asia, 2013 -
- video / shapify.me / bibtex -

Our system allows users to create textured 3D models of themselves in arbitrary poses using only a single 3D sensor.

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- - 		- - Intrinsic Scene Properties from a Single RGB-D Image - -
- Jonathan T. Barron, Jitendra Malik -
- CVPR, 2013   (Oral Presentation) -
- supplement / bibtex / talk / keynote (or powerpoint, PDF) / code & data -

By embedding mixtures of shapes & lights into a soft segmentation of an image, and by leveraging the output of the Kinect, we can extend SIRFS to scenes. -
-
TPAMI Journal version: version / bibtex -

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- Boundary_png - - - Boundary Cues for 3D Object Shape Recovery - -
- Kevin Karsch, - Zicheng Liao, - Jason Rock, - Jonathan T. Barron, - Derek Hoiem -
- CVPR, 2013 -
- supplement / bibtex -

Boundary cues (like occlusions and folds) can be used for shape reconstruction, which improves object recognition for humans and computers.

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- - -
- - 		- - Color Constancy, Intrinsic Images, and Shape Estimation - -
- Jonathan T. Barron, Jitendra Malik -
- ECCV, 2012 -
- supplement / - bibtex / - poster / - video -

This paper is subsumed by SIRFS.

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- - 		- - Shape, Albedo, and Illumination from a Single Image of an Unknown Object - -
- Jonathan T. Barron, Jitendra Malik -
- CVPR, 2012 -
- supplement / - bibtex / - poster -

This paper is subsumed by SIRFS.

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- b3do - - - A Category-Level 3-D Object Dataset: Putting the Kinect to Work - -
- Allison Janoch, - Sergey Karayev, - Yangqing Jia, - Jonathan T. Barron, - Mario Fritz, - Kate Saenko, - Trevor Darrell -
- ICCV 3DRR Workshop, 2011 -
- bibtex / - "smoothing" code -

We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.

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- safs_small - - - High-Frequency Shape and Albedo from Shading using Natural Image Statistics - -
- Jonathan T. Barron, Jitendra Malik -
- CVPR, 2011 -
- bibtex -

This paper is subsumed by SIRFS.

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- fast-texture - - - Discovering Efficiency in Coarse-To-Fine Texture Classification - -
- Jonathan T. Barron, Jitendra Malik -
- Technical Report, 2010 -
- bibtex -

A model and feature representation that allows for sub-linear coarse-to-fine semantic segmentation. -

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- prl - - - Parallelizing Reinforcement Learning - -
- Jonathan T. Barron, Dave Golland, Nicholas J. Hay -
- Technical Report, 2009 -
- bibtex -

Markov Decision Problems which lie in a low-dimensional latent space can be decomposed, allowing modified RL algorithms to run orders of magnitude faster in parallel.

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- blind-date - - - Blind Date: Using Proper Motions to Determine the Ages of Historical Images - -
- Jonathan T. Barron, David W. Hogg, Dustin Lang, Sam Roweis -
- The Astronomical Journal, 136, 2008 -

Using the relative motions of stars we can accurately estimate the date of origin of historical astronomical images.

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- clean-usnob - - - Cleaning the USNO-B Catalog Through Automatic Detection of Optical Artifacts - -
- Jonathan T. Barron, Christopher Stumm, David W. Hogg, Dustin Lang, Sam Roweis -
- The Astronomical Journal, 135, 2008 -

We use computer vision techniques to identify and remove diffraction spikes and reflection halos in the USNO-B Catalog.

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In use at Astrometry.net

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Miscellanea

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Micropapers

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- 		- Squareplus: A Softplus-Like Algebraic Rectifier -
- A Convenient Generalization of Schlick's Bias and Gain Functions -
- Continuously Differentiable Exponential Linear Units -
- Scholars & Big Models: How Can Academics Adapt? -
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Recorded Talks

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- 		- Radiance Fields and the Future of Generative Media, 2025
- View Dependent Podcast, 2024
- Bay Area Robotics Symposium, 2023 -
- EGSR Keynote, 2021
- TUM AI Lecture Series, 2020
- Vision & Graphics Seminar at MIT, 2020 -
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Academic Service

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- 		- Lead Area Chair, ICCV 2025 -
- Lead Area Chair, CVPR 2025 -
- Area Chair, CVPR 2024 -
- Demo Chair, CVPR 2023 -
- Area Chair, CVPR 2022 -
- Area Chair & Award Committee Member, CVPR 2021 -
- Area Chair, CVPR 2019 -
- Area Chair, CVPR 2018 -
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Teaching

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- 		- Graduate Student Instructor, CS188 Spring 2011 -
- Graduate Student Instructor, CS188 Fall 2010 -
- Figures, "Artificial Intelligence: A Modern Approach", 3rd Edition -
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- Feel free to steal this website's source code. Do not scrape the HTML from this page itself, as it includes analytics tags that you do not want on your own website — use the github code instead. Also, consider using Leonid Keselman's Jekyll fork of this page. -

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