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<!DOCTYPE HTML>
<html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<!-- Hi, Jon Here. Please DELETE the two <script> tags below if you use this HTML, otherwise my analytics will track your page -->
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-7580334-2"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-7580334-2');
</script>
<title>Jon Barron</title>
<meta name="author" content="Jon Barron">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
<link rel="icon" type="image/png" href="images/seal_icon.png">
</head>
<body>
<table style="width:100%;max-width:800px;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
<td style="padding:0px">
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
<td style="padding:2.5%;width:63%;vertical-align:middle">
<p style="text-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 machine learning.
</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 style="text-align:center">
<a href="mailto:jonbarron@gmail.com">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="https://www.facebook.com/barron.jon">Social</a>
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<a href="images/JonBarron.jpg"><img style="width:100%;max-width:100%" alt="profile photo" src="images/JonBarron_circle.jpg" class="hoverZoomLink"></a>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>Research</heading>
<p>
I'm interested in computer vision, machine learning, optimization, and image processing. 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>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr onmouseout="nerf_stop()" onmouseover="nerf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/vase_small.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/vase_still.png' width="160">
</div>
<script type="text/javascript">
function nerf_start() {
document.getElementById('nerf_image').style.opacity = "1";
}
function nerf_stop() {
document.getElementById('nerf_image').style.opacity = "0";
}
nerf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://www.matthewtancik.com/nerf">
<papertitle>NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
</papertitle>
</a>
<br>
<a href="https://people.eecs.berkeley.edu/~bmild/">Ben Mildenhall*</a>,
<a href="https://people.eecs.berkeley.edu/~pratul/">Pratul Srinivasan*</a>,
<a href="http://matthewtancik.com/">Matthew Tancik*</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://cseweb.ucsd.edu/~ravir/">Ravi Ramamoorthi</a>,
<a href="https://www2.eecs.berkeley.edu/Faculty/Homepages/yirenng.html">Ren Ng</a>
<br>
<em>arXiv</em>, 2020
<br>
<a href="http://www.matthewtancik.com/nerf">project page</a>
/
<a href="https://arxiv.org/abs/2003.08934">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=JuH79E8rdKc">video</a>
/
<a href="https://github.com/bmild/nerf">code</a>
<p></p>
<p>
Training a tiny non-convolutional neural network to reproduce a scene using volume rendering achieves photorealistic view synthesis.</p>
</td>
</tr>
<tr onmouseout="porshadmanip_stop()" onmouseover="porshadmanip_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='porshadmanip_image'>
<img src='images/porshadmanip_after.jpg' width="160"></div>
<img src='images/porshadmanip_before.jpg' width="160">
</div>
<script type="text/javascript">
function porshadmanip_start() {
document.getElementById('porshadmanip_image').style.opacity = "1";
}
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}
porshadmanip_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2005.08925">
<papertitle>Portrait Shadow Manipulation</papertitle>
</a>
<br>
<a href="https://people.eecs.berkeley.edu/~cecilia77/">Xuaner (Cecilia) Zhang</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://ai.google/research/people/105312/">Yun-Ta Tsai</a>,
<a href="https://www.linkedin.com/in/rohit-pandey-bab10b7a/">Rohit Pandey</a>,
<a href="http://people.csail.mit.edu/xiuming/">Xiuming Zhang</a>,
<a href="http://graphics.stanford.edu/~renng/">Ren Ng</a>,
<a href="http://graphics.stanford.edu/~dejacobs/">David E. Jacobs</a>
<br>
<em>SIGGRAPH</em>, 2020
<br>
<a href="https://people.eecs.berkeley.edu/~cecilia77/project-pages/portrait">project page</a> /
<a href="https://www.youtube.com/watch?v=M_qYTXhzyac">video</a>
<p></p>
<p>Networks can be trained to remove shadows cast on human faces and to soften harsh lighting.</p>
</td>
</tr>
<tr onmouseout="learnaf_stop()" onmouseover="learnaf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='learnaf_image'>
<img src='images/learnaf_after.jpg' width="160"></div>
<img src='images/learnaf_before.jpg' width="160">
</div>
<script type="text/javascript">
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learnaf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2004.12260">
<papertitle>Learning to Autofocus</papertitle>
</a>
<br>
<a href="">Charles Herrmann</a>,
<a href="">Richard Strong Bowen</a>,
<a href="http://nealwadhwa.com">Neal Wadhwa</a>,
<a href="http://rahuldotgarg.appspot.com/">Rahul Garg</a>,
<a href="https://scholar.google.com/citations?user=BxqV_RsAAAAJ">Qiurui He</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://www.cs.cornell.edu/~rdz/index.htm">Ramin Zabih</a>
<br>
<em>CVPR</em>, 2020
<br>
<a href="https://arxiv.org/abs/2004.12260">arXiv</a>
<p></p>
<p>Machine learning can be used to train cameras to autofocus (which is not the same problem as "depth from defocus").</p>
</td>
</tr>
<tr onmouseout="lighthouse_stop()" onmouseover="lighthouse_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='lh_image'><video width=100% height=100% muted autoplay loop>
<source src="images/rings_crop.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/rings.png' width="160">
</div>
<script type="text/javascript">
function lighthouse_start() {
document.getElementById('lh_image').style.opacity = "1";
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://people.eecs.berkeley.edu/~pratul/lighthouse/">
<papertitle>Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination</papertitle>
</a>
<br>
<a href="https://people.eecs.berkeley.edu/~pratul/">Pratul Srinivasan*</a>,
<a href="https://people.eecs.berkeley.edu/~bmild/">Ben Mildenhall*</a>,
<a href="http://matthewtancik.com/">Matthew Tancik</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://research.google/people/RichardTucker/">Richard Tucker</a>,
<a href="https://www.cs.cornell.edu/~snavely/">Noah Snavely</a>
<br>
<em>CVPR</em>, 2020
<br>
<a href="https://people.eecs.berkeley.edu/~pratul/lighthouse/">project page</a>
/
<a href="https://github.com/pratulsrinivasan/lighthouse">code</a>
/
<a href="https://arxiv.org/abs/2003.08367">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=KsiZpUFPqIU">video</a>
<p></p>
<p>We predict a volume from an input stereo pair that can be used to calculate incident lighting at any 3D point within a scene.</p>
</td>
</tr>
<tr onmouseout="nightsight_stop()" onmouseover="nightsight_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nightsight_image'><img src='images/nightsight_after.jpg'></div>
<img src='images/nightsight_before.jpg'>
</div>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/1910.11336">
<papertitle>Handheld Mobile Photography in Very Low Light</papertitle>
</a>
<br>
<a href="https://sites.google.com/site/orlylibaprofessional/">Orly Liba</a>,
<a href="https://scholar.google.com/citations?user=6PhlPWMAAAAJ">Kiran Murthy</a>,
<a href="https://ai.google/research/people/105312/">Yun-Ta Tsai</a>,
<a href="https://www.timothybrooks.com/">Timothy Brooks</a>,
<a href="https://people.csail.mit.edu/tfxue/">Tianfan Xue</a>,
<a href="https://scholar.google.com/citations?user=qgc_jY0AAAAJ">Nikhil Karnad</a>,
<a href="https://scholar.google.com/citations?user=BxqV_RsAAAAJ">Qiurui He</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://ai.google/research/people/105641/">Dillon Sharlet</a>,
<a href="http://www.geisswerks.com/">Ryan Geiss</a>,
<a href="https://people.csail.mit.edu/hasinoff/">Samuel W. Hasinoff</a>,
<a href="https://scholar.google.com/citations?user=2jXxOYQAAAAJ">Yael Pritch</a>,
<a href="http://graphics.stanford.edu/~levoy/">Marc Levoy</a>
<br>
<em>SIGGRAPH Asia</em>, 2019
<br>
<a href="https://github.com/google/night-sight/tree/master/docs">project page</a>
<br>
<p></p>
<p>By rethinking metering, white balance, and tone mapping, we can take pictures in places too dark for humans to see clearly.</p>
</td>
</tr>
<tr onmouseout="font_stop()" onmouseover="font_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='font_image'><img src='images/font_after.png'></div>
<img src='images/font_before.png'>
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/1910.00748">
<papertitle>A Deep Factorization of Style and Structure in Fonts</papertitle>
</a>
<br>
<a href="http://www.cs.cmu.edu/~asrivats/">Nikita Srivatsan</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://people.eecs.berkeley.edu/~klein/">Dan Klein</a>,
<a href="http://cseweb.ucsd.edu/~tberg/">Taylor Berg-Kirkpatrick</a>
<br>
<em>EMNLP</em>, 2019   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<p></p>
<p>Variational auto-encoders can be used to disentangle a characters style from its content.</p>
</td>
</tr>
<tr onmouseout="dpzlearn_stop()" onmouseover="dpzlearn_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<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">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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>,
<a href="">Sameer Ansari,</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>ICCV</em>, 2019   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://github.com/google-research/google-research/tree/master/dual_pixels">code</a> /
<a href="data/GargICCV2019.bib">bibtex</a>
<p></p>
<p>Considering the optics of dual-pixel image sensors improves monocular depth estimation techniques.</p>
</td>
</tr>
<tr onmouseout="porlight_stop()" onmouseover="porlight_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<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">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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>,
<a href="https://ai.google/research/people/105312/">Yun-Ta Tsai</a>,
<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="https://petapixel.com/2019/07/16/researchers-developed-an-ai-that-can-relight-portraits-after-the-fact/">press</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="loss_stop()" onmouseover="loss_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<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">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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, Best Paper Award Finalist)</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://www.youtube.com/watch?v=4IInDT_S0ow&t=37m22s">talk</a> /
<a href="https://drive.google.com/file/d/1GzRYRIfLHvNLT_QwjHoBjHkBbs3Nbf0x/view?usp=sharing">slides</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="mpi_stop()" onmouseover="mpi_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<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">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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, Best Paper Award Finalist)</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="unprocessing_stop()" onmouseover="unprocessing_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<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">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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="https://github.com/google-research/google-research/tree/master/unprocessing">code</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 style="padding:20px;width:25%;vertical-align:middle">
<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">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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="https://github.com/google-research/google-research/tree/master/motion_blur">code</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="darkflash_stop()" onmouseover="darkflash_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<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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darkflash_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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 style="padding:20px;width:25%;vertical-align:middle">
<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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motionstereo_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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 style="padding:20px;width:25%;vertical-align:middle">
<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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document.getElementById('portrait_image').style.opacity = "1";
}
function portrait_stop() {
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portrait_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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 style="padding:20px;width:25%;vertical-align:middle">
<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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aperture_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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 style="padding:20px;width:25%;vertical-align:middle">
<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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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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 style="padding:20px;width:25%;vertical-align:middle">
<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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function friendly_stop() {
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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 style="padding:20px;width:25%;vertical-align:middle">
<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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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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 style="padding:20px;width:25%;vertical-align:middle">
<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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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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>,
<a href="https://ai.google/research/people/105312/">Yun-Ta Tsai</a>,
<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://store.google.com/product/pixel_compare">Google Pixel</a>, <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 style="padding:20px;width:25%;vertical-align:middle">
<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">
function jump_start() {
document.getElementById('jump_image').style.opacity = "1";
}
function jump_stop() {
document.getElementById('jump_image').style.opacity = "0";
}
jump_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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>
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<tr onmouseout="hdrp_stop()" onmouseover="hdrp_start()">
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<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">
function hdrp_start() {
document.getElementById('hdrp_image').style.opacity = "1";
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document.getElementById('hdrp_image').style.opacity = "0";
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<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>
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<td style="padding:20px;width:25%;vertical-align:middle">
<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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document.getElementById('bs_image').style.opacity = "1";
}
function bs_stop() {
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<td style="padding:20px;width:75%;vertical-align:middle">
<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>
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<tr onmouseout="diverdi_stop()" onmouseover="diverdi_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<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 style="padding:20px;width:75%;vertical-align:middle">
<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>
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