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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html>
<head>
<!-- Please delete this script if you use this HTML. -->
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-7580334-1"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag() {
dataLayer.push(arguments);
}
gtag('js', new Date());
gtag('config', 'UA-7580334-1');
</script>
<meta name="viewport" content="width=500">
<link href="stylesheet.css" rel="stylesheet" type="text/css">
<link rel="icon" type="image/png" href="images/seal_icon.png">
<title>Jon Barron</title>
<meta http-equiv="Content-Type" content="text/html; charset=us-ascii">
<link href='https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic' rel='stylesheet' type='text/css'>
</head>
<body>
<table width="800" border="0" align="center" cellspacing="0" cellpadding="0">
<tr>
<td>
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20">
<tr>
<td width="67%" valign="middle">
<p align="center">
<name>Jon Barron</name>
</p>
<p>I am a staff research scientist at <a href="https://ai.google/research">Google Research</a>, where I work on computer vision and computational photography.
</p>
<p>
At Google I've worked on <a href="https://ai.googleblog.com/2014/04/lens-blur-in-new-google-camera-app.html">Lens Blur</a>, <a href="https://ai.googleblog.com/2014/10/hdr-low-light-and-high-dynamic-range.html">HDR+</a>, <a href="https://www.google.com/get/cardboard/jump/">Jump</a>, <a href="https://ai.googleblog.com/2017/10/portrait-mode-on-pixel-2-and-pixel-2-xl.html">Portrait Mode</a>, and <a href="https://www.youtube.com/watch?v=JSnB06um5r4">Glass</a>. I did my PhD at <a href="http://www.eecs.berkeley.edu/">UC Berkeley</a>, where I was advised by <a href="http://www.cs.berkeley.edu/~malik/">Jitendra Malik</a> and funded by the <a href="http://www.nsfgrfp.org/">NSF GRFP</a>. I did my bachelors at the <a href="http://cs.toronto.edu">University of Toronto</a>.
</p>
<p align=center>
<a href="mailto:[email protected]">Email</a>  / 
<a href="data/JonBarron-CV.pdf">CV</a>  / 
<a href="data/JonBarron-bio.txt">Biography</a>  / 
<a href="https://scholar.google.com/citations?hl=en&user=jktWnL8AAAAJ">Google Scholar</a>  / 
<a href="http://www.linkedin.com/in/jonathanbarron/"> LinkedIn </a>
</p>
</td>
<td width="33%">
<img src="images/JonBarron_circle.jpg">
</td>
</tr>
</table>
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20">
<tr>
<td width="100%" valign="middle">
<heading>Research</heading>
<p>
I'm interested in computer vision, machine learning, optimization, image processing, and photography. Much of my research is about inferring the physical world (shape, depth, motion, paint, light, colors, etc) from images. Representative papers are <span class="highlight">highlighted</span>.
</p>
</td>
</tr>
</table>
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20">
<tr onmouseout="porlight_stop()" onmouseover="porlight_start()">
<td width="25%">
<div class="one">
<div class="two" id='porlight_image'><img src='images/porlight_after.jpg'></div>
<img src='images/porlight_before.jpg'>
</div>
<script type="text/javascript">
function porlight_start() {
document.getElementById('porlight_image').style.opacity = "1";
}
function porlight_stop() {
document.getElementById('porlight_image').style.opacity = "0";
}
porlight_stop()
</script>
</td>
<td valign="middle" width="75%">
<a href="https://arxiv.org/abs/1905.00824">
<papertitle>Single Image Portrait Relighting</papertitle>
</a>
<br>
<a href="http://kevinkingo.com/">Tiancheng Sun</a>,
<strong>Jonathan T. Barron</strong>, Yun-Ta Tsai,
<a href="https://cseweb.ucsd.edu/~zex014/">Zexiang Xu</a>, Xueming Yu,
<a href="http://ict.usc.edu/profile/graham-fyffe/">Graham Fyffe</a>, Christoph Rhemann, Jay Busch,
<a href="https://www.pauldebevec.com/">Paul Debevec</a>,
<a href="https://cseweb.ucsd.edu/~ravir/">Ravi Ramamoorthi</a>
<br>
<em>SIGGRAPH</em>, 2019
<br>
<a href="https://www.youtube.com/watch?v=yxhGWds_g4I">video</a> /
<a href="data/SunSIGGRAPH2019.bib">bibtex</a>
<br>
<p></p>
<p>Training a neural network on light stage scans and environment maps produces an effective relighting method.</p>
</td>
</tr>
<tr onmouseout="dpzlearn_stop()" onmouseover="dpzlearn_start()">
<td width="25%">
<div class="one">
<div class="two" id='dpzlearn_image'><img src='images/dpzlearn_after.jpg'></div>
<img src='images/dpzlearn_before.jpg'>
</div>
<script type="text/javascript">
function dpzlearn_start() {
document.getElementById('dpzlearn_image').style.opacity = "1";
}
function dpzlearn_stop() {
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dpzlearn_stop()
</script>
</td>
<td valign="middle" width="75%">
<a href="https://arxiv.org/abs/1904.05822">
<papertitle>Learning Single Camera Depth Estimation using Dual-Pixels</papertitle>
</a>
<br>
<a href="http://rahuldotgarg.appspot.com/">Rahul Garg</a>,
<a href="http://nealwadhwa.com">Neal Wadhwa</a>, Sameer Ansari,
<strong>Jonathan T. Barron</strong>
<br> arXiv:1904.05822
<br>
<p></p>
<p>Considering the optics of dual-pixel image sensors improves monocular depth estimation techniques.</p>
</td>
</tr>
<tr onmouseout="unprocessing_stop()" onmouseover="unprocessing_start()">
<td width="25%">
<div class="one">
<div class="two" id='unprocessing_image'><img src='images/unprocessing_after.jpg'></div>
<img src='images/unprocessing_before.jpg'>
</div>
<script type="text/javascript">
function unprocessing_start() {
document.getElementById('unprocessing_image').style.opacity = "1";
}
function unprocessing_stop() {
document.getElementById('unprocessing_image').style.opacity = "0";
}
unprocessing_stop()
</script>
</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1H0Wtd--un2JN76dUJN8iC9fWfkA16n8D/view?usp=sharing">
<papertitle>Unprocessing Images for Learned Raw Denoising</papertitle>
</a>
<br>
<a href="http://timothybrooks.com/">Tim Brooks</a>,
<a href="https://people.eecs.berkeley.edu/~bmild/">Ben Mildenhall</a>,
<a href="https://people.csail.mit.edu/tfxue/">Tianfan Xue</a>,
<a href="http://people.csail.mit.edu/jiawen/">Jiawen Chen</a>,
<a href="http://www.dsharlet.com/">Dillon Sharlet</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>CVPR</em>, 2019   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://arxiv.org/abs/1811.11127">arxiv</a> /
<a href="http://timothybrooks.com/tech/unprocessing/">project page</a> /
<a href="data/BrooksCVPR2019.bib">bibtex</a>
<p></p>
<p>We can learn a better denoising model by processing and unprocessing images the same way a camera does.</p>
</td>
</tr>
<tr onmouseout="motionblur_stop()" onmouseover="motionblur_start()">
<td width="25%">
<div class="one">
<div class="two" id='motionblur_image'><img src='images/motionblur_after.jpg'></div>
<img src='images/motionblur_before.jpg'>
</div>
<script type="text/javascript">
function motionblur_start() {
document.getElementById('motionblur_image').style.opacity = "1";
}
function motionblur_stop() {
document.getElementById('motionblur_image').style.opacity = "0";
}
motionblur_stop()
</script>
</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1hWpA4f6iLVcOkZI3zEAAWKARSQhnVgbY/view?usp=sharing">
<papertitle>Learning to Synthesize Motion Blur</papertitle>
</a>
<br>
<a href="http://timothybrooks.com/">Tim Brooks</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>CVPR</em>, 2019   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://arxiv.org/abs/1811.11745">arxiv</a> /
<a href="https://drive.google.com/file/d/1dUQwBMmQdYYIP0zHR_nDQY-uQbaMdcSN/view?usp=sharing">supplement</a> /
<a href="http://timothybrooks.com/tech/motion-blur/">project page</a> /
<a href="https://www.youtube.com/watch?v=8T1jjSz-2V8">video</a> /
<a href="data/BrooksBarronCVPR2019.bib">bibtex</a>
<p></p>
<p>Frame interpolation techniques can be used to train a network that directly synthesizes linear blur kernels.</p>
</td>
</tr>
<tr onmouseout="mpi_stop()" onmouseover="mpi_start()">
<td width="25%">
<div class="one">
<div class="two" id='mpi_image'><img src='images/mpi_after.jpg'></div>
<img src='images/mpi_before.jpg'>
</div>
<script type="text/javascript">
function mpi_start() {
document.getElementById('mpi_image').style.opacity = "1";
}
function mpi_stop() {
document.getElementById('mpi_image').style.opacity = "0";
}
mpi_stop()
</script>
</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1TU5L6fnt4Kd49IUOU7aNxor5NIgdHuNG/view?usp=sharing">
<papertitle>Pushing the Boundaries of View Extrapolation with Multiplane Images</papertitle>
</a>
<br>
<a href="https://people.eecs.berkeley.edu/~pratul/">Pratul P. Srinivasan</a>, Richard Tucker,
<strong>Jonathan T. Barron</strong>,
<a href="http://cseweb.ucsd.edu/~ravir/">Ravi Ramamoorthi</a>,
<a href="http://graphics.stanford.edu/~renng/">Ren Ng</a>,
<a href="https://www.cs.cornell.edu/~snavely/">Noah Snavely</a>
<br>
<em>CVPR</em>, 2019   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://drive.google.com/file/d/1GUW_n-BAn9Q4VntEA_OTHNJiHO7XfC62/view?usp=sharing">supplement</a> /
<a href="https://www.youtube.com/watch?v=aJqAaMNL2m4">video</a> /
<a href="data/SrinivasanCVPR2019.bib">bibtex</a>
<p></p>
<p>View extrapolation with multiplane images works better if you reason about disocclusions and disparity sampling frequencies.</p>
</td>
</tr>
<tr onmouseout="loss_stop()" onmouseover="loss_start()" bgcolor="#ffffd0">
<td width="25%">
<div class="one">
<div class="two" id='loss_image'><img src='images/loss_after.png'></div>
<img src='images/loss_before.png'>
</div>
<script type="text/javascript">
function loss_start() {
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</script>
</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/open?id=1xpZ0fL9h1y9RfcTyPgVkxUrF3VwdkBvq">
<papertitle>A General and Adaptive Robust Loss Function</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>
<br>
<em>CVPR</em>, 2019   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://arxiv.org/abs/1701.03077">arxiv</a> /
<a href="https://drive.google.com/open?id=1HNveL7xSNh6Ss7sxLK8Mw2L1Fc-rRhL4">supplement</a> /
<a href="https://youtu.be/BmNKbnF69eY">video</a> /
<a href="https://github.com/google-research/google-research/tree/master/robust_loss">tensorflow code</a> /
<a href="https://github.com/jonbarron/robust_loss_pytorch">pytorch code</a> /
<a href="data/BarronCVPR2019_reviews.txt">reviews</a> /
<a href="data/BarronCVPR2019.bib">bibtex</a>
<p></p>
<p>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.</p>
</td>
</tr>
<tr onmouseout="darkflash_stop()" onmouseover="darkflash_start()">
<td width="25%">
<div class="one">
<div class="two" id='darkflash_image'><img src='images/darkflash_after.png'></div>
<img src='images/darkflash_before.png'>
</div>
<script type="text/javascript">
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}
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darkflash_stop()
</script>
</td>
<td valign="middle" width="75%">
<a href="https://arxiv.org/abs/1901.01370">
<papertitle>Stereoscopic Dark Flash for Low-light Photography</papertitle>
</a>
<br>
<a href="https://www.andrew.cmu.edu/user/jianwan2/">Jian Wang</a>,
<a href="https://people.csail.mit.edu/tfxue/">Tianfan Xue</a>,
<strong>Jonathan T. Barron</strong>
<a href="http://people.csail.mit.edu/jiawen/">Jiawen Chen</a>
<br>
<em>ICCP</em>, 2019
<br>
<p></p>
<p>
By making one camera in a stereo pair hyperspectral we can multiplex dark flash pairs in space instead of time.
</p>
</td>
</tr>
<tr onmouseout="motionstereo_stop()" onmouseover="motionstereo_start()">
<td width="25%">
<div class="one">
<div class="two" id='motionstereo_image'><img src='images/motionstereo_after.png'></div>
<img src='images/motionstereo_before.png'>
</div>
<script type="text/javascript">
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</script>
</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1AABFJ3NgD5DAo5JEpEjWZrcQNzjZnvW9/view?usp=sharing">
<papertitle>Depth from Motion for Smartphone AR</papertitle>
</a>
<br>
<a href="https://www.linkedin.com/in/valentinjulien/">Julien Valentin</a>,
<a href="https://www.linkedin.com/in/adarshkowdle/">Adarsh Kowdle</a>,
<strong>Jonathan T. Barron</strong>, <a href="http://nealwadhwa.com">Neal Wadhwa</a>, and others
<br>
<em>SIGGRAPH Asia</em>, 2018
<br>
<a href="data/Valentin2018.bib">bibtex</a>
<p></p>
<p>Depth cues from camera motion allow for real-time occlusion effects in augmented reality applications.</p>
</td>
</tr>
<tr onmouseout="portrait_stop()" onmouseover="portrait_start()">
<td width="25%">
<div class="one">
<div class="two" id='portrait_image'><img src='images/portrait_after.jpg'></div>
<img src='images/portrait_before.jpg'>
</div>
<script type="text/javascript">
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}
function portrait_stop() {
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portrait_stop()
</script>
</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/13i6DlS9UhGVKmwslLUFnKBwdxFRVQeQj/view?usp=sharing">
<papertitle>Synthetic Depth-of-Field with a Single-Camera Mobile Phone</papertitle>
</a>
<br>
<a href="http://nealwadhwa.com">Neal Wadhwa</a>,
<a href="http://rahuldotgarg.appspot.com/">Rahul Garg</a>,
<a href="http://graphics.stanford.edu/~dejacobs/">David E. Jacobs</a>, Bryan E. Feldman, Nori Kanazawa, Robert Carroll,
<a href="http://www.cs.cmu.edu/~ymovshov/">Yair Movshovitz-Attias</a>,
<strong>Jonathan T. Barron</strong>, Yael Pritch,
<a href="http://graphics.stanford.edu/~levoy/">Marc Levoy</a>
<br>
<em>SIGGRAPH</em>, 2018
<br>
<a href="https://arxiv.org/abs/1806.04171">arxiv</a> /
<a href="https://ai.googleblog.com/2017/10/portrait-mode-on-pixel-2-and-pixel-2-xl.html">blog post</a> /
<a href="data/Wadhwa2018.bib">bibtex</a>
<p></p>
<p>Dual pixel cameras and semantic segmentation algorithms can be used for shallow depth of field effects.</p>
<p>This system is the basis for "Portrait Mode" on the Google Pixel 2 smartphones</p>
</td>
</tr>
<tr onmouseout="aperture_stop()" onmouseover="aperture_start()">
<td width="25%">
<div class="one">
<div class="two" id='aperture_image'><img src='images/aperture_after.jpg'></div>
<img src='images/aperture_before.jpg'>
</div>
<script type="text/javascript">
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function aperture_stop() {
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</script>
</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1MpvxcW7OTJP321QL_q4ZLQ8D653bZZzy/view?usp=sharing">
<papertitle>Aperture Supervision for Monocular Depth Estimation</papertitle>
</a>
<br>
<a href="https://people.eecs.berkeley.edu/~pratul/">Pratul P. Srinivasan</a>,
<a href="http://rahuldotgarg.appspot.com/">Rahul Garg</a>,
<a href="http://nealwadhwa.com">Neal Wadhwa</a>,
<a href="http://graphics.stanford.edu/~renng/">Ren Ng</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>CVPR</em>, 2018
<br>
<a href="https://github.com/google/aperture_supervision">code</a> /
<a href="data/Srinivasan2018.bib">bibtex</a>
<p></p>
<p>Varying a camera's aperture provides a supervisory signal that can teach a neural network to do monocular depth estimation.</p>
</td>
</tr>
<tr onmouseout="deepburst_stop()" onmouseover="deepburst_start()">
<td width="25%">
<div class="one">
<div class="two" id='deepburst_image'><img src='images/deepburst_after.png'></div>
<img src='images/deepburst_before.png'>
</div>
<script type="text/javascript">
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</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1GAH8ijyZ7GnoBnQFANEzdXinHrE4vvXn/view?usp=sharing">
<papertitle>Burst Denoising with Kernel Prediction Networks</papertitle>
</a>
<br>
<a href="https://people.eecs.berkeley.edu/~bmild/">Ben Mildenhall</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://people.csail.mit.edu/jiawen/">Jiawen Chen</a>,
<a href="http://www.dsharlet.com/">Dillon Sharlet</a>,
<a href="http://graphics.stanford.edu/~renng/">Ren Ng</a>, Robert Carroll
<br>
<em>CVPR</em>, 2018   <font color=#FF8080><strong>(Spotlight)</strong></font>
<br>
<a href="https://drive.google.com/file/d/1aqk3Q-L2spjLZh2yRWKUWIDcZkGjQ7US/view?usp=sharing">supplement</a> /
<a href="https://github.com/google/burst-denoising">code</a> /
<a href="data/Mildenhall2018.bib">bibtex</a>
<p></p>
<p>We train a network to predict linear kernels that denoise noisy bursts from cellphone cameras.</p>
</td>
</tr>
<tr onmouseout="friendly_stop()" onmouseover="friendly_start()">
<td width="25%">
<div class="one">
<div class="two" id='friendly_image'><img src='images/friendly_after.png'></div>
<img src='images/friendly_before.png'>
</div>
<script type="text/javascript">
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</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1w_0djhL0QgC_fbehnJ0c-J23_kW_420p/view?usp=sharing">
<papertitle>A Hardware-Friendly Bilateral Solver for Real-Time Virtual Reality Video</papertitle>
</a>
<br>
<a href="https://homes.cs.washington.edu/~amrita/">Amrita Mazumdar</a>, <a href="http://homes.cs.washington.edu/~armin/">Armin Alaghi</a>, <strong>Jonathan T. Barron</strong>, <a href="https://www.cs.unc.edu/~gallup/">David Gallup</a>, <a href="https://homes.cs.washington.edu/~luisceze/">Luis Ceze</a>, <a href="https://homes.cs.washington.edu/~oskin/">Mark Oskin</a>, <a href="http://homes.cs.washington.edu/~seitz/">Steven M. Seitz</a>
<br>
<em>High-Performance Graphics (HPG)</em>, 2017
<br>
<a href="https://sampa.cs.washington.edu/projects/vr-hw.html">project page</a>
<p></p>
<p>A reformulation of the bilateral solver can be implemented efficiently on GPUs and FPGAs.</p>
</td>
</tr>
<tr onmouseout="hdrnet_stop()" onmouseover="hdrnet_start()">
<td width="25%">
<div class="one">
<div class="two" id='hdrnet_image'><img src='images/hdrnet_after.jpg'></div>
<img src='images/hdrnet_before.jpg'>
</div>
<script type="text/javascript">
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</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1jQY3CTMnLX7PeGUzYLso9H1eCsZyWbwg/view?usp=sharing">
<papertitle>Deep Bilateral Learning for Real-Time Image Enhancement</papertitle>
</a>
<br>
<a href="http://www.mgharbi.com">Michaël Gharbi</a>, <a href="http://people.csail.mit.edu/jiawen/">Jiawen Chen</a>, <strong>Jonathan T. Barron</strong>, <a href="https://people.csail.mit.edu/hasinoff/">Samuel W. Hasinoff</a>, <a href="http://people.csail.mit.edu/fredo/">Frédo Durand </a>
<br>
<em>SIGGRAPH</em>, 2017
<br>
<a href="https://groups.csail.mit.edu/graphics/hdrnet/">project page</a> /
<a href="https://www.youtube.com/watch?v=GAe0qKKQY_I">video</a> /
<a href="data/GharbiSIGGRAPH2017.bib">bibtex</a> /
<a href="http://news.mit.edu/2017/automatic-image-retouching-phone-0802">p</a><a href="https://www.wired.com/story/googles-new-algorithm-perfects-photos-before-you-even-take-them/">r</a><a href="https://petapixel.com/2017/08/02/new-ai-can-retouch-photos-snap/">e</a><a href="https://www.theverge.com/2017/8/2/16082272/google-mit-retouch-photos-machine-learning">s</a><a href="http://gizmodo.com/clever-camera-app-uses-deep-learning-to-perfectly-retou-1797474282">s</a>
<p></p>
<p>By training a deep network in bilateral space we can learn a model for high-resolution and real-time image enhancement.</p>
</td>
</tr>
<tr onmouseout="ffcc_stop()" onmouseover="ffcc_start()">
<td width="25%">
<div class="one">
<div class="two" id='ffcc_image'><img src='images/ffcc_after.jpg'></div>
<img src='images/ffcc_before.jpg'>
</div>
<script type="text/javascript">
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</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1VDWAS7HgiufTNPP7CQY00KmJP71QIZAy/view?usp=sharing">
<papertitle>Fast Fourier Color Constancy</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>, Yun-Ta Tsai
<br>
<em>CVPR</em>, 2017
<br>
<a href="https://drive.google.com/file/d/1b5zdR5UYPTkXa2UgiLhi-PP89bzINJSR/view?usp=sharing">supplement</a> /
<a href="https://youtu.be/rZCXSfl13rY">video</a> /
<a href="data/BarronTsaiCVPR2017.bib">bibtex</a> /
<a href="https://github.com/google/ffcc">code</a> /
<a href="https://drive.google.com/open?id=0B4nuwEMaEsnmWkJQMlFPSFNzbEk">output</a> /
<a href="https://blog.google/products/photos/six-tips-make-your-photos-pop/">blog post</a> /
<a href="https://9to5google.com/2017/03/03/google-photos-auto-white-balance/">p</a><a href="https://www.engadget.com/2017/03/03/google-photos-automatically-fixes-your-pictures-white-balance/">r</a><a href="https://lifehacker.com/google-photos-will-now-automatically-adjust-the-white-b-1793009155">e</a><a href="https://petapixel.com/2017/03/06/google-photos-will-now-automatically-white-balance-snapshots/">s</a><a href="http://www.theverge.com/2017/3/3/14800062/google-photos-auto-white-balance-android">s</a>
<p></p>
<p>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.</p>
<p>This technology is used by <a href="https://photos.google.com/">Google Photos</a> and <a href="https://www.google.com/maps">Google Maps</a>.</p>
</td>
</tr>
<tr onmouseout="jump_stop()" onmouseover="jump_start()" bgcolor="#ffffd0">
<td width="25%">
<div class="one">
<div class="two" id='jump_image'><img src='images/jump_anim.gif'></div>
<img src='images/jump_still.png'>
</div>
<script type="text/javascript">
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function jump_stop() {
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</script>
</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1RBnTrtzqmuO8uj3GQaR5vBJZjIC3Jxjn/view?usp=sharing">
<papertitle>Jump: Virtual Reality Video</papertitle>
</a>
<br>
<a href="http://mi.eng.cam.ac.uk/~ra312/">Robert Anderson</a>, <a href="https://www.cs.unc.edu/~gallup/">David Gallup</a>, <strong>Jonathan T. Barron</strong>, <a href="https://mediatech.aalto.fi/~janne/index.php">Janne Kontkanen</a>, <a href="https://www.cs.cornell.edu/~snavely/">Noah Snavely</a>, <a href="http://carlos-hernandez.org/">Carlos Hernández</a>, <a href="https://homes.cs.washington.edu/~sagarwal/">Sameer Agarwal</a>, <a href="https://homes.cs.washington.edu/~seitz/">Steven M Seitz</a>
<br>
<em>SIGGRAPH Asia</em>, 2016
<br>
<a href="https://drive.google.com/file/d/11D4eCDXqqFTtZT0WS2COJE0hsAN3QEww/view?usp=sharing">supplement</a> /
<a href="https://www.youtube.com/watch?v=O0qUYynupTI">video</a> /
<a href="data/Anderson2016.bib">bibtex</a> /
<a href="https://blog.google/products/google-vr/jump-using-omnidirectional-stereo-vr-video/">blog post</a>
<p></p>
<p>Using computer vision and a ring of cameras, we can make video for virtual reality headsets that is both stereo and 360°.</p>
<p>This technology is used by <a href="https://vr.google.com/jump/">Jump</a>. </p>
</td>
</tr>
<tr onmouseout="hdrp_stop()" onmouseover="hdrp_start()">
<td width="25%">
<div class="one">
<div class="two" id='hdrp_image'><img src='images/hdrp_after.jpg'></div>
<img src='images/hdrp_before.jpg'>
</div>
<script type="text/javascript">
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</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1SSSmVHWbMQ7sZMOredSVWVJXbXobkyzA/view?usp=sharing">
<papertitle>Burst Photography for High Dynamic Range and Low-Light Imaging on Mobile Cameras</papertitle>
</a>
<br>
<a href="http://people.csail.mit.edu/hasinoff/">Samuel W. Hasinoff</a>, <a href="http://www.dsharlet.com/">Dillon Sharlet</a>, <a href="http://www.geisswerks.com/">Ryan Geiss</a>, <a href="http://people.csail.mit.edu/abadams/">Andrew Adams</a>, <strong>Jonathan T. Barron</strong>, Florian Kainz, <a href="http://people.csail.mit.edu/jiawen/">Jiawen Chen</a>, <a href="http://graphics.stanford.edu/~levoy/">Marc Levoy</a>
<br>
<em>SIGGRAPH Asia</em>, 2016
<br>
<a href="http://hdrplusdata.org/">project page</a> /
<a href="https://drive.google.com/open?id=15EUuSDi1BtHUgQCaiooVrD44qYKIC3vx">supplement</a> /
<a href="data/Hasinoff2016.bib">bibtex</a>
<p></p>
<p>Mobile phones can take beautiful photographs in low-light or high dynamic range environments by aligning and merging a burst of images.</p>
<p>This technology is used by the <a href="https://research.googleblog.com/2014/10/hdr-low-light-and-high-dynamic-range.html">Nexus HDR+</a> feature.</p>
</td>
</tr>
<tr onmouseout="bs_stop()" onmouseover="bs_start()" bgcolor="#ffffd0">
<td width="25%">
<div class="one">
<div class="two" id='bs_image'><img src='images/BS_after.jpg'></div>
<img src='images/BS_before.jpg'>
</div>
<script type="text/javascript">
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</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1zFzCaFwkGK1EGmJ_KEqb-ZsRJhfUKN2S/view?usp=sharing">
<papertitle>The Fast Bilateral Solver</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>,
<a href="https://cs.stanford.edu/~poole/">Ben Poole</a>
<br>
<em>ECCV</em>, 2016   <font color="red"><strong>(Best Paper Honorable Mention)</strong></font>
<br>
<a href="http://arxiv.org/abs/1511.03296">arXiv</a> /
<a href="https://drive.google.com/file/d/0B4nuwEMaEsnmdEREcjhlSXM2NGs/view?usp=sharing">supplement</a> /
<a href="data/BarronPooleECCV2016.bib">bibtex</a> /
<a href="http://videolectures.net/eccv2016_barron_bilateral_solver/">video (they messed up my slides, use →)</a> /
<a href="https://drive.google.com/file/d/19x1AeN0PFus6Pjrd8nR-vCmJ6bNEefsC/view?usp=sharing">keynote</a> (or <a href="https://drive.google.com/file/d/1p9nduiymK9jUh7WfwlsMjBfW8RoNe_61/view?usp=sharing">PDF</a>) /
<a href="https://github.com/poolio/bilateral_solver">code</a> /
<a href="https://drive.google.com/file/d/0B4nuwEMaEsnmaDI3bm5VeDRxams/view?usp=sharing">depth super-res results</a> /
<a href="data/BarronPooleECCV2016_reviews.txt">reviews</a>
<p></p>
<p>Our solver smooths things better than other filters and faster than other optimization algorithms, and you can backprop through it.</p>
</td>
</tr>
<tr onmouseout="diverdi_stop()" onmouseover="diverdi_start()">
<td width="25%">
<div class="one">
<div class="two" id='diverdi_image'><img src='images/diverdi_after.jpg'></div>
<img src='images/diverdi_before.jpg'>
</div>
<script type="text/javascript">
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</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1mmT-LuK_eBZsl3qp4-fAshEPdgfbgvNE/view?usp=sharing">
<papertitle>Geometric Calibration for Mobile, Stereo, Autofocus Cameras</papertitle>
</a>
<br>
<a href="http://www.stephendiverdi.com/">Stephen DiVerdi</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>WACV</em>, 2016
<br>
<a href="data/Diverdi2016.bib">bibtex</a>
<p></p>
<p>Standard techniques for stereo calibration don't work for cheap mobile cameras.</p>
</td>
</tr>
<tr onmouseout="dt_stop()" onmouseover="dt_start()">
<td width="25%">
<div class="one">
<div class="two" id='dt_image'><img src='images/DT_edge.jpg'></div>
<img src='images/DT_image.jpg'>
</div>
<script type="text/javascript">
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</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/178Xj2PZ1w6hZJpucU-TiZOoCemJmvsVQ/view?usp=sharing">
<papertitle>Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform</papertitle>
</a>
<br>
<em>CVPR</em>, 2016
<br>
<a href="http://liangchiehchen.com/">Liang-Chieh Chen</a>, <strong>Jonathan T. Barron</strong>, <a href="http://ttic.uchicago.edu/~gpapan/">George Papandreou</a>, <a href="http://www.cs.ubc.ca/~murphyk/">Kevin Murphy</a>, <a href="http://www.stat.ucla.edu/~yuille/">Alan L. Yuille</a>
<br>
<a href="data/Chen2016.bib">bibtex</a> /
<a href="http://liangchiehchen.com/projects/DeepLab.html">project page</a> /
<a href="https://bitbucket.org/aquariusjay/deeplab-public-ver2">code</a>
<p></p>
<p>By integrating an edge-aware filter into a convolutional neural network we can learn an edge-detector while improving semantic segmentation.</p>
</td>
</tr>
<tr onmouseout="ccc_stop()" onmouseover="ccc_start()" bgcolor="#ffffd0">
<td width="25%">
<div class="one">
<div class="two" id='ccc_image'><img src='images/ccc_after.jpg'></div>
<img src='images/ccc_before.jpg'>
</div>
<script type="text/javascript">
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</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1id74VNDL8ACrrWf6vYgN2M4kS8gd4n7w/view?usp=sharing">
<papertitle>Convolutional Color Constancy</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>
<br>
<em>ICCV</em>, 2015
<br>
<a href="https://drive.google.com/file/d/1vO3sVOMihmpNqsuASeR46Y_iME0lOANR/view?usp=sharing">supplement</a> / <a href="data/BarronICCV2015.bib">bibtex</a> / <a href="https://youtu.be/saHwKY9rfx0">video</a> (or <a href="https://drive.google.com/file/d/0B4nuwEMaEsnmalBNUzlENUJSVDg/view?usp=sharing">mp4</a>)
<p></p>
<p>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%.</p>
</td>
</tr>
<tr>
<td width="25%">
<img src='images/Shelhamer2015.jpg'>
</td>
<td valign="middle" width="75%">
<a href="https://drive.google.com/file/d/1stygV71uBruD7Ck9CaAQr7nREvr3DtUL/view?usp=sharing">
<papertitle>Scene Intrinsics and Depth from a Single Image</papertitle>
</a>
<br>
<a href="http://imaginarynumber.net/">Evan Shelhamer</a>, <strong>Jonathan T. Barron</strong>, <a href="http://www.eecs.berkeley.edu/%7Etrevor/">Trevor Darrell</a>
<br>
<em>ICCV Workshop</em>, 2015
<br>
<a href="data/Shelhamer2015.bib">bibtex</a>
<p></p>
<p>The monocular depth estimates produced by fully convolutional networks can be used to inform intrinsic image estimation.</p>
</td>
</tr>
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<td width="25%">
<div id='lens_blurry' class='hidden'><img src="images/BarronCVPR2015_anim.gif"></div>
<div id='lens_sharp'>
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<a href="https://drive.google.com/file/d/1R4RdaBZIs-uJobhIFs9yKf3jIsaHQNH0/view?usp=sharing">
<papertitle>Fast Bilateral-Space Stereo for Synthetic Defocus</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>, <a href="http://people.csail.mit.edu/abadams/">Andrew Adams</a>, <a href="http://people.csail.mit.edu/yichangshih/">YiChang Shih</a>, <a href="http://carlos-hernandez.org/">Carlos Hernández</a>
<br>
<em>CVPR</em>, 2015   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://drive.google.com/file/d/125qgMdqeT1vojMIijIKcOF099LjUgUOL/view?usp=sharing">abstract</a> /
<a href="https://drive.google.com/file/d/1HGGvVOGxmPjvgdK5q3UD1Qb5Nttg6kq9/view?usp=sharing">supplement</a> /
<a href="data/BarronCVPR2015.bib">bibtex</a> /
<a href="http://techtalks.tv/talks/fast-bilateral-space-stereo-for-synthetic-defocus/61624/">talk</a> /
<a href="https://drive.google.com/file/d/0B4nuwEMaEsnmSzZZdUJSMllSUkE/view?usp=sharing">keynote</a> (or <a href="https://drive.google.com/open?id=0B4nuwEMaEsnmZ1ZXUzBCWDJYeFU">PDF</a>)
<p></p>
<p>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.</p>
<p>This technology is used by the <a href="http://googleresearch.blogspot.com/2014/04/lens-blur-in-new-google-camera-app.html">Google Camera "Lens Blur"</a> feature. </p>
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<img src="images/PABMM2015.jpg" alt="PontTuset" width="160" style="border-style: none">
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<a href="https://drive.google.com/file/d/1EUvfslce9iqCbJ_IAE4J1ser6hpse_uh/view?usp=sharing" id="MCG_journal">
<papertitle>Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation</papertitle>
</a>
<br>
<a href="http://imatge.upc.edu/web/people/jordi-pont-tuset">Jordi Pont-Tuset</a>, <a href="http://www.cs.berkeley.edu/~arbelaez/">Pablo Arbeláez</a>, <strong>Jonathan T. Barron</strong>, <a href="http://imatge.upc.edu/web/ferran">Ferran Marqués</a>, <a href="http://www.cs.berkeley.edu/~malik/">Jitendra Malik</a>
<br>
<em>TPAMI</em>, 2017
<br>
<a href="http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/">project page</a> /
<a href="data/PontTusetTPAMI2017.bib">bibtex</a> /
<a href="https://drive.google.com/file/d/1AiB78Fy7QVA3KqgcooyzMAC5L8HhNzjz/view?usp=sharing">fast eigenvector code</a>
<p></p>
<p>We produce state-of-the-art contours, regions and object candidates, and we compute normalized-cuts eigenvectors 20× faster.</p>
<p>This paper subsumes our CVPR 2014 paper.</p>
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<p>
<a href="https://drive.google.com/file/d/1RvyCiDMg--jyO8lLBvopp0o271LvREoa/view?usp=sharing" id="SIRFS">
<papertitle>Shape, Illumination, and Reflectance from Shading</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>, <a href="http://www.cs.berkeley.edu/~malik/">Jitendra Malik</a>
<br>
<em>TPAMI</em>, 2015
<br>
<a href="https://drive.google.com/file/d/1D3k6u4Ek2dWm2Yf7kl1Vu_g1HFSxztqF/view?usp=sharing">supplement</a> / <a href="data/BarronMalikTPAMI2015.bib">bibtex</a> / <a href="https://drive.google.com/file/d/0B4nuwEMaEsnmVWpfa19mbUxIYW8/view?usp=sharing">keynote</a> (or <a href="https://drive.google.com/file/d/0B4nuwEMaEsnmazJvLXJUb0NuM1U/view?usp=sharing">powerpoint</a>, <a href="https://drive.google.com/file/d/0B4nuwEMaEsnmTDBUWE96VHJndjg/view?usp=sharing">PDF</a>) / <a href="http://www.youtube.com/watch?v=NnePYprvFvA">video</a> / <a href="https://drive.google.com/file/d/1vg9Rb-kBntSTnTCzVgFlskkPXvTB_5aq/view?usp=sharing">code & data</a> / <a href="data/why_did_this_paper_come_out_in_2015.txt">rant</a> / <a href="https://drive.google.com/file/d/11X5Zfjy7Q7oP_V2rtqy2f5-x9YgQUAFd/view?usp=sharing">kudos</a>
</p>
<p>
We present <strong>SIRFS</strong>, which can estimate shape, chromatic illumination, reflectance, and shading from a single image of an masked object.
</p>
<p>
This paper subsumes our CVPR 2011, CVPR 2012, and ECCV 2012 papers.
</p>
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<img src="images/ArbalaezCVPR2014.jpg" alt="ArbalaezCVPR2014" width="160" height="120" style="border-style: none">
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<td width="75%" valign="middle">
<a href="https://drive.google.com/file/d/1M0wijHY134F9ETBgO8mjeuKUSblTRLG0/view?usp=sharing">
<papertitle>Multiscale Combinatorial Grouping</papertitle>
</a>
<br>
<a href="http://www.cs.berkeley.edu/~arbelaez/">Pablo Arbeláez</a>, <a href="http://imatge.upc.edu/web/people/jordi-pont-tuset">Jordi Pont-Tuset</a>, <strong>Jonathan T. Barron</strong>, <a href="http://imatge.upc.edu/web/ferran">Ferran Marqués</a>, <a href="http://www.cs.berkeley.edu/~malik/">Jitendra Malik</a>
<br>
<em>CVPR</em>, 2014
<br>
<a href="http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/">project page</a> /
<a href="data/ArbelaezCVPR2014.bib">bibtex</a>
<p>This paper is subsumed by <a href="#MCG_journal">our journal paper</a>.</p>
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<a href="https://drive.google.com/file/d/1shvItvx_8Sb8QNXhrOXkuRmx2618iwNJ/view?usp=sharing">
<papertitle>Volumetric Semantic Segmentation using Pyramid Context Features</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>, <a href="http://www.cs.berkeley.edu/~arbelaez/">Pablo Arbeláez</a>, <a href="http://big.lbl.gov/">Soile V. E. Keränen</a>, <a href="http://www.lbl.gov/gsd/biggin.html">Mark D. Biggin</a>,
<br> <a href="http://dwknowles.lbl.gov/">David W. Knowles</a>, <a href="http://www.cs.berkeley.edu/~malik/">Jitendra Malik</a>
<br>
<em>ICCV</em>, 2013
<br>
<a href="https://drive.google.com/file/d/1htiLpMAcYLtuBthmAb4XHnOYxUbkfnqR/view?usp=sharing">supplement</a> /
<a href="https://drive.google.com/file/d/1qoYeFNa443myn2SfcdhmCsYBqE9xQrPD/view?usp=sharing">poster</a> /
<a href="data/BarronICCV2013.bib">bibtex</a> / <a href="http://www.youtube.com/watch?v=Y56-FcfnlVA&hd=1">video 1</a> (or <a href="https://drive.google.com/file/d/0B4nuwEMaEsnmZ1ZLaHdQYzAxNlU/view?usp=sharing">mp4</a>) / <a href="http://www.youtube.com/watch?v=mvRoYuP6-l4&hd=1">video 2</a> (or <a href="https://drive.google.com/file/d/0B4nuwEMaEsnmZ1ZLaHdQYzAxNlU/view?usp=sharing">mp4</a>) / <a href="https://drive.google.com/file/d/0B4nuwEMaEsnmSF9YdWJjQmh4QW8/view?usp=sharing">code & data</a>
<p>
We present a technique for efficient per-voxel linear classification, which enables accurate and fast semantic segmentation of volumetric Drosophila imagery.
</p>
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<a href="https://drive.google.com/file/d/0B4nuwEMaEsnmbG1tOGIta3N1Wjg/view?usp=sharing" id="3DSP">
<papertitle>3D Self-Portraits</papertitle>
</a>
<br>
<a href="http://www.hao-li.com/">Hao Li</a>, <a href="http://www.evouga.com/">Etienne Vouga</a>, Anton Gudym, <a href="http://www.cs.princeton.edu/~linjiel/">Linjie Luo</a>, <strong>Jonathan T. Barron</strong>, Gleb Gusev
<br>
<em>SIGGRAPH Asia</em>, 2013
<br>
<a href="http://www.youtube.com/watch?v=DmUkbZ0QMCA">video</a> / <a href="http://shapify.me/">shapify.me</a> / <a href="data/3DSP_siggraphAsia2013.bib">bibtex</a>
<p>Our system allows users to create textured 3D models of themselves in arbitrary poses using only a single 3D sensor.</p>
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