Jon Barron

I'm a 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, and Maps. I did my PhD at UC Berkeley, where I was advised by Jitendra Malik. I've received the PAMI Young Researcher Award.

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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.

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.

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*
arXiv, 2024
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.

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*
project page / video / arXiv

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.

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.

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

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

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

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

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

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

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

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

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

A survey of recent progress in diffusion models for images, videos, and 3D.

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

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

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

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

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

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
project page / video / arXiv

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

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
project page / video / arXiv

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

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.

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.

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

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

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

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

SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image Collections
Mark Boss, Andreas Engelhardt, Abhishek Kar, Yuanzhen Li, Deqing Sun, Jonathan T. Barron, Hendrik P. A. Lensch, Varun Jampani
NeurIPS, 2022
project page / video / arXiv

A joint optimization framework for estimating shape, BRDF, camera pose, and illumination from in-the-wild image collections.

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.

Fast and High-Quality Image Denoising via Malleable Convolutions
Yifan Jiang, Bartlomiej Wronski, Ben Mildenhall,
Jonathan T. Barron, Zhangyang Wang, Tianfan Xue
ECCV, 2022
project page / 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.

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.

NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images
Ben Mildenhall, Peter Hedman, Ricardo Martin-Brualla,
Pratul Srinivasan, Jonathan T. Barron
CVPR, 2022   (Oral Presentation)
project page / arXiv / video

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.

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.

Advances in Neural Rendering
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
State of the Art Report at EUROGRAPHICS, 2022

A survey of recent progress in neural rendering.

Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition
Mark Boss, Varun Jampani, Raphael Braun,
Ce Liu, Jonathan T. Barron, Hendrik P. A. Lensch
NeurIPS, 2021
project page / video / arXiv

Replacing a costly illumination integral with a simple network query enables more accurate novel view-synthesis and relighting compared to NeRD.

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.

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)

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
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
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
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
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
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 / 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.

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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
cs188 Graduate Student Instructor, CS188 Spring 2011
Graduate Student Instructor, CS188 Fall 2010
Figures, "Artificial Intelligence: A Modern Approach", 3rd Edition

Blog Posts

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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