Jon Barron

I am a staff research scientist at Google Research, where I work on computer vision and machine learning.

At Google I've worked on Lens Blur, HDR+, Jump, Portrait Mode, and Glass. I did my PhD at UC Berkeley, where I was advised by Jitendra Malik and funded by the NSF GRFP. I did my bachelors at the University of Toronto. I've received the C.V. Ramamoorthy Distinguished Research Award and the PAMI Young Researcher Award.

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I'm interested in computer vision, machine learning, optimization, and image processing. Much of my research is about inferring the physical world (shape, motion, color, light, etc) from images. Representative papers are highlighted.

Learned Dual-View Reflection Removal
Simon Niklaus, Xuaner (Cecilia) Zhang, Jonathan T. Barron, Neal Wadhwa, Rahul Garg, Feng Liu, Tianfan Xue,
arXiv, 2020
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
arXiv, 2020
project page / arXiv / video

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

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
arXiv, 2020
project page / arXiv / video

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

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 / 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)

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)
project page / arXiv / talk video / supp video / code

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
project page / video

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

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

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
project page

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

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
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

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
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

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

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

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

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

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.

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

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

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

This technology is used by Jump.

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

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

This technology is used by the Nexus HDR+ feature.

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

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

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

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

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

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

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

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%.

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

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

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)

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.

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

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

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

This paper subsumes our CVPR 2014 paper.

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

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

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

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.

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.

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

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

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

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.

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

This paper is subsumed by SIRFS.

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.

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.

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

This paper is subsumed by SIRFS.

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

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

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

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.

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.

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.

In use at

Area Chair, CVPR 2019

Area Chair, CVPR 2018
cs188 Graduate Student Instructor, CS188 Spring 2011

Graduate Student Instructor, CS188 Fall 2010

Figures, "Artificial Intelligence: A Modern Approach", 3rd Edition

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