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<!DOCTYPE HTML>
<html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<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 senior 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/2020/12/portrait-light-enhancing-portrait.html">Portrait Light</a>, <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>, <a href="https://www.youtube.com/watch?v=JSnB06um5r4">Glass</a>, and <a href="https://www.matthewtancik.com/nerf">NeRF</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've received the <a href="https://www2.eecs.berkeley.edu/Students/Awards/15/">C.V. Ramamoorthy Distinguished Research Award</a> and the <a href="https://www.thecvf.com/?page_id=413#YRA">PAMI Young Researcher Award</a>.
</p>
<p style="text-align:center">
<a href="mailto:[email protected]">Email</a>  / 
<a href="data/JonBarron-CV.pdf">CV</a>  / 
<a href="data/JonBarron-bio.txt">Bio</a>  / 
<a href="https://scholar.google.com/citations?hl=en&user=jktWnL8AAAAJ">Google Scholar</a>  / 
<a href="https://twitter.com/jon_barron">Twitter</a>  / 
<a href="https://github.com/jonbarron/">Github</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, motion, color, light, 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="npil_stop()" onmouseover="npil_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='npil_image'>
<img src='images/npil_after.jpg' width="160"></div>
<img src='images/npil_before.jpg' width="160">
</div>
<script type="text/javascript">
function npil_start() {
document.getElementById('npil_image').style.opacity = "1";
}
function npil_stop() {
document.getElementById('npil_image').style.opacity = "0";
}
npil_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://markboss.me/publication/2021-neural-pil/">
<papertitle>Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition</papertitle>
</a>
<br>
<a href="https://markboss.me">Mark Boss</a>,
<a href="https://varunjampani.github.io">Varun Jampani</a>,
<a href="https://uni-tuebingen.de/en/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/computergrafik/lehrstuhl/mitarbeiter/raphael-braun/">Raphael Braun</a>,
<a href="http://people.csail.mit.edu/celiu/">Ce Liu</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://uni-tuebingen.de/en/faculties/faculty-of-science/departments/computer-science/lehrstuehle/computergrafik/computer-graphics/staff/prof-dr-ing-hendrik-lensch/">Hendrik P. A. Lensch</a>
<br>
<em>NeurIPS</em>, 2021
<br>
<a href="https://markboss.me/publication/2021-neural-pil/">project page</a> /
<a href="https://www.youtube.com/watch?v=p5cKaNwVp4M">video</a> /
<a href="https://arxiv.org/abs/2110.14373">arXiv</a>
<p></p>
<p>
Replacing a costly illumination integral with a simple network query enables more accurate novel view-synthesis and relighting compared to NeRD.
</p>
</td>
</tr>
<tr onmouseout="hypernerf_stop()" onmouseover="hypernerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='hypernerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/hypernerf_after.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/hypernerf_before.jpg' width="160">
</div>
<script type="text/javascript">
function hypernerf_start() {
document.getElementById('hypernerf_image').style.opacity = "1";
}
function hypernerf_stop() {
document.getElementById('hypernerf_image').style.opacity = "0";
}
hypernerf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://hypernerf.github.io/">
<papertitle>HyperNeRF: A Higher-Dimensional Representation
for Topologically Varying Neural Radiance Fields</papertitle>
</a>
<br>
<a href="https://keunhong.com">Keunhong Park</a>,
<a href="https://utkarshsinha.com">Utkarsh Sinha</a>,
<a href="https://phogzone.com/">Peter Hedman</a>,
<strong>Jonathan T. Barron</strong>, <br>
<a href="http://sofienbouaziz.com">Sofien Bouaziz</a>,
<a href="https://www.danbgoldman.com">Dan B Goldman</a>,
<a href="http://www.ricardomartinbrualla.com">Ricardo Martin-Brualla</a>,
<a href="https://homes.cs.washington.edu/~seitz/">Steven M. Seitz</a>
<br>
<em>SIGGRAPH Asia</em>, 2021
<br>
<a href="https://hypernerf.github.io/">project page</a>
/
<a href="https://arxiv.org/abs/2106.13228">arXiv</a>
<p></p>
<p>Applying ideas from level set methods to NeRF lets you represent scenes that deform and change shape.</p>
</td>
</tr>
<tr onmouseout="nerfactor_stop()" onmouseover="nerfactor_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nerfactor_image'>
<img src='images/nerfactor_after.png' width="160"></div>
<img src='images/nerfactor_before.png' width="160">
</div>
<script type="text/javascript">
function nerfactor_start() {
document.getElementById('nerfactor_image').style.opacity = "1";
}
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document.getElementById('nerfactor_image').style.opacity = "0";
}
nerfactor_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://people.csail.mit.edu/xiuming/projects/nerfactor/">
<papertitle>NeRFactor: Neural Factorization of Shape and Reflectance<br>
Under an Unknown Illumination</papertitle>
</a>
<br>
<a href="https://people.csail.mit.edu/xiuming/">Xiuming Zhang</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<a href="https://boyangdeng.com/">Boyang Deng</a>,<br>
<a href="https://www.pauldebevec.com/">Paul Debevec</a>,
<a href="http://billf.mit.edu/">William T. Freeman</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>SIGGRAPH Asia</em>, 2021
<br>
<a href="https://people.csail.mit.edu/xiuming/projects/nerfactor/">project page</a>
/
<a href="https://arxiv.org/abs/2106.01970">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=UUVSPJlwhPg">video</a>
<p></p>
<p>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.</p>
</td>
<tr onmouseout="dualfont_stop()" onmouseover="dualfont_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='dualfont_image'><img src='images/dualfont_after.png'></div>
<img src='images/dualfont_before.png'>
</div>
<script type="text/javascript">
function dualfont_start() {
document.getElementById('dualfont_image').style.opacity = "1";
}
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}
dualfont_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2109.06627">
<papertitle>Scalable Font Reconstruction with Dual Latent Manifolds</papertitle>
</a>
<br>
<a href="http://www.cs.cmu.edu/~asrivats/">Nikita Srivatsan</a>,
<a href="http://siwu.io/">Si Wu</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://cseweb.ucsd.edu/~tberg/">Taylor Berg-Kirkpatrick</a>
<br>
<em>EMNLP</em>, 2021
<br>
<p></p>
<p>VAEs can be used to disentangle a font's style from its content, and to generalize to characters that were never observed during training.</p>
</td>
</tr>
<tr onmouseout="mipnerf_stop()" onmouseover="mipnerf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='mipnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/mipnerf_ipe_yellow.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/mipnerf_ipe_yellow.png' width="160">
</div>
<script type="text/javascript">
function mipnerf_start() {
document.getElementById('mipnerf_image').style.opacity = "1";
}
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document.getElementById('mipnerf_image').style.opacity = "0";
}
mipnerf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://jonbarron.info/mipnerf">
<papertitle>Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="http://matthewtancik.com/">Matthew Tancik</a>, <br>
<a href="https://phogzone.com/">Peter Hedman</a>,
<a href="http://www.ricardomartinbrualla.com/">Ricardo Martin-Brualla</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>
<br>
<em>ICCV</em>, 2021   <font color="red"><strong>(Oral Presentation, Best Paper Honorable Mention)</strong></font>
<br>
<a href="http://jonbarron.info/mipnerf">project page</a>
/
<a href="https://arxiv.org/abs/2103.13415">arXiv</a>
/
<a href="https://youtu.be/EpH175PY1A0">video</a>
/
<a href="https://github.com/google/mipnerf">code</a>
<p></p>
<p>NeRF is aliased, but we can anti-alias it by casting cones and prefiltering the positional encoding function.</p>
</td>
</tr>
<tr onmouseout="nerfbake_stop()" onmouseover="nerfbake_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nerfbake_image'><video width=100% height=100% muted autoplay loop>
<source src="images/nerfbake_15.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/nerfbake_160.png' width="160">
</div>
<script type="text/javascript">
function nerfbake_start() {
document.getElementById('nerfbake_image').style.opacity = "1";
}
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}
nerfbake_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://nerf.live">
<papertitle>Baking Neural Radiance Fields for Real-Time View Synthesis</papertitle>
</a>
<br>
<a href="https://phogzone.com/">Peter Hedman</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://www.pauldebevec.com/">Paul Debevec</a>
<br>
<em>ICCV</em>, 2021   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="http://nerf.live">project page</a>
/
<a href="https://arxiv.org/abs/2103.14645">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=5jKry8n5YO8">video</a>
/
<a href="https://nerf.live/#demos">demo</a>
<p></p>
<p>Baking a trained NeRF into a sparse voxel grid of colors and features lets you render it in real-time in your browser.</p>
</td>
<tr onmouseout="nerfie_stop()" onmouseover="nerfie_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nerfie_image'><video width=100% height=100% muted autoplay loop>
<source src="images/nerfie_after.mp4" type="video/mp4">
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</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://nerfies.github.io/">
<papertitle>Nerfies: Deformable Neural Radiance Fields</papertitle>
</a>
<br>
<a href="https://keunhong.com">Keunhong Park</a>,
<a href="https://utkarshsinha.com">Utkarsh Sinha</a>,
<strong>Jonathan T. Barron</strong>, <br>
<a href="http://sofienbouaziz.com">Sofien Bouaziz</a>,
<a href="https://www.danbgoldman.com">Dan B Goldman</a>,
<a href="https://homes.cs.washington.edu/~seitz/">Steven M. Seitz</a>,
<a href="http://www.ricardomartinbrualla.com">Ricardo-Martin Brualla</a>
<br>
<em>ICCV</em>, 2021   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://nerfies.github.io/">project page</a> /
<a href="https://arxiv.org/abs/2011.12948">arXiv</a> /
<a href="https://www.youtube.com/watch?v=MrKrnHhk8IA">video</a>
<p></p>
<p>Building deformation fields into NeRF lets you capture non-rigid subjects, like people.
</p>
</td>
</tr>
<tr onmouseout="c5_stop()" onmouseover="c5_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='c5_image'>
<img src='images/c5_after.jpg' width="160"></div>
<img src='images/c5_before.jpg' width="160">
</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/2011.11890">
<papertitle>Cross-Camera Convolutional Color Constancy</papertitle>
</a>
<br>
<a href="https://sites.google.com/corp/view/mafifi">Mahmoud Afifi</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://www.chloelegendre.com/">Chloe LeGendre</a>,
<a href="https://research.google/people/105312/">Yun-Ta Tsai</a>,
<a href="https://www.linkedin.com/in/fbleibel/">Francois Bleibel</a>
<br>
<em>ICCV</em>, 2021   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<p></p>
<p>
With some extra (unlabeled) test-set images, you can build a hypernetwork that calibrates itself at test time to previously-unseen cameras.
</p>
</td>
</tr>
<tr onmouseout="dualdefocus_stop()" onmouseover="dualdefocus_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='dualdefocus_image'>
<img src='images/dualdefocus_after.jpg' width="160"></div>
<img src='images/dualdefocus_before.jpg' width="160">
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://imaging.cs.cmu.edu/dual_pixels/">
<papertitle>Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image</papertitle>
</a>
<br>
<a href="https://shumianxin.github.io/">Shumian Xin</a>,
<a href="http://nealwadhwa.com">Neal Wadhwa</a>,
<a href="https://people.csail.mit.edu/tfxue/">Tianfan Xue</a>,
<strong>Jonathan T. Barron</strong>, <br>
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<a href="http://people.csail.mit.edu/jiawen/">Jiawen Chen</a>,
<a href="https://www.cs.cmu.edu/~igkioule/">Ioannis Gkioulekas</a>,
<a href="http://rahuldotgarg.appspot.com/">Rahul Garg</a>
<br>
<em>ICCV</em>, 2021   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://imaging.cs.cmu.edu/dual_pixels/">project page</a> /
<a href="https://github.com/cmu-ci-lab/dual_pixel_defocus_estimation_deblurring">code</a>
<br>
<p></p>
<p>
Multiplane images can be used to simultaneously deblur dual-pixel images, despite variable defocus due to depth variation in the scene.
</p>
</td>
</tr>
<tr onmouseout="nerd_stop()" onmouseover="nerd_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nerd_image'><video width=100% height=100% muted autoplay loop>
<source src="images/nerd_160.mp4" type="video/mp4">
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<img src='images/nerd_160.jpg' width="160">
</div>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://markboss.me/publication/2021-nerd/">
<papertitle>NeRD: Neural Reflectance Decomposition from Image Collections</papertitle>
</a>
<br>
<a href="https://markboss.me">Mark Boss</a>,
<a href="https://uni-tuebingen.de/en/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/computergrafik/lehrstuhl/mitarbeiter/raphael-braun/">Raphael Braun</a>,
<a href="https://varunjampani.github.io">Varun Jampani</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://people.csail.mit.edu/celiu/">Ce Liu</a>,
<a href="https://uni-tuebingen.de/en/faculties/faculty-of-science/departments/computer-science/lehrstuehle/computergrafik/computer-graphics/staff/prof-dr-ing-hendrik-lensch/">Hendrik P. A. Lensch</a>
<br>
<em>ICCV</em>, 2021
<br>
<a href="https://markboss.me/publication/2021-nerd/">project page</a> /
<a href="https://www.youtube.com/watch?v=JL-qMTXw9VU">video</a> /
<a href="https://github.com/cgtuebingen/NeRD-Neural-Reflectance-Decomposition">code</a> /
<a href="https://arxiv.org/abs/2012.03918">arXiv</a>
<p></p>
<p>
A NeRF-like model that can decompose (and mesh) objects with non-Lambertian reflectances, complex geometry, and unknown illumination.
</p>
</td>
</tr>
<tr onmouseout="flare_stop()" onmouseover="flare_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='flare_image'>
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</div>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2011.12485">
<papertitle>How to Train Neural Networks for Flare Removal</papertitle>
</a>
<br>
<a href="http://yicheng.rice.edu/">Yicheng Wu</a>,
<a href="https://scholar.google.com/citations?user=BxqV_RsAAAAJ">Qiurui He</a>,
<a href="https://people.csail.mit.edu/tfxue/">Tianfan Xue</a>,
<a href="http://rahuldotgarg.appspot.com/">Rahul Garg</a>, <br>
<a href="http://people.csail.mit.edu/jiawen/">Jiawen Chen</a>,
<a href="https://computationalimaging.rice.edu/team/ashok-veeraraghavan/">Ashok Veeraraghavan</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>ICCV</em>, 2021
<br>
<a href="https://yichengwu.github.io/flare-removal/">project page</a> /
<a href="https://arxiv.org/abs/2011.12485">arXiv</a>
<p></p>
<p>
Simulating the optics of a camera's lens lets you train a model that removes lens flare from a single image.
</p>
</td>
</tr>
<tr onmouseout="inerf_stop()" onmouseover="inerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='inerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/inerf_after.mp4" type="video/mp4">
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<img src='images/inerf_before.jpg' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://yenchenlin.me/inerf/">
<papertitle>iNeRF: Inverting Neural Radiance Fields for Pose Estimation</papertitle>
</a>
<br>
<a href="https://yenchenlin.me/">Lin Yen-Chen</a>,
<a href="http://www.peteflorence.com/">Pete Florence</a>,
<strong>Jonathan T. Barron</strong>, <br>
<a href="https://meche.mit.edu/people/faculty/[email protected]">Alberto Rodriguez</a>,
<a href="http://web.mit.edu/phillipi/">Phillip Isola</a>,
<a href="https://scholar.google.com/citations?user=_BPdgV0AAAAJ&hl=en">Tsung-Yi Lin</a>
<br>
<em>IROS</em>, 2021
<br>
<a href="http://yenchenlin.me/inerf/">project page</a> /
<a href="https://arxiv.org/abs/2012.05877">arXiv</a> /
<a href="https://www.youtube.com/watch?v=eQuCZaQN0tI">video</a>
<p></p>
<p>Given an image of an object and a NeRF of that object, you can estimate that object's pose.
</p>
</td>
</tr>
<tr onmouseout="ibrnet_stop()" onmouseover="ibrnet_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='ibrnet_image'><video width=100% height=100% muted autoplay loop>
<source src="images/ibrnet_after.mp4" type="video/mp4">
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<img src='images/ibrnet_before.jpg' width="160">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://ibrnet.github.io/">
<papertitle>IBRNet: Learning Multi-View Image-Based Rendering</papertitle>
</a>
<br>
<a href="https://www.cs.cornell.edu/~qqw/">Qianqian Wang</a>,
<a href="https://www.linkedin.com/in/zhicheng-wang-96116897/">Zhicheng Wang</a>,
<a href="https://www.kylegenova.com/">Kyle Genova</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<a href="https://scholar.google.com/citations?user=Rh9T3EcAAAAJ&hl=en">Howard Zhou</a>, <br>
<strong>Jonathan T. Barron</strong>,
<a href="http://www.ricardomartinbrualla.com/">Ricardo Martin-Brualla</a>,
<a href="https://www.cs.cornell.edu/~snavely/">Noah Snavely</a>,
<a href="https://www.cs.princeton.edu/~funk/">Thomas Funkhouser</a>
<br>
<em>CVPR</em>, 2021
<br>
<a href="https://ibrnet.github.io/">project page</a> /
<a href="https://github.com/googleinterns/IBRNet">code</a> /
<a href="https://arxiv.org/abs/2102.13090">arXiv</a>
<p></p>
<p>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%.</p>
</td>
</tr>
<tr onmouseout="nerv_stop()" onmouseover="nerv_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nerv_image'><video width=100% height=100% muted autoplay loop>
<source src="images/hotdog.mp4" type="video/mp4">
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</video></div>
<img src='images/hotdog.jpg' width="160">
</div>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://pratulsrinivasan.github.io/nerv/">
<papertitle>NeRV: Neural Reflection and Visibility Fields for Relighting and View Synthesis</papertitle>
</a>
<br>
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<a href="https://boyangdeng.com/">Boyang Deng</a>,
<a href="https://people.csail.mit.edu/xiuming/">Xiuming Zhang</a>, <br>
<a href="http://matthewtancik.com/">Matthew Tancik</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>CVPR</em>, 2021
<br>
<a href="https://pratulsrinivasan.github.io/nerv/">project page</a> /
<a href="https://www.youtube.com/watch?v=4XyDdvhhjVo">video</a> /
<a href="https://arxiv.org/abs/2012.03927">arXiv</a>
<p></p>
<p>Using neural approximations of expensive visibility integrals lets you recover relightable NeRF-like models.</p>
</td>
</tr>
<tr onmouseout="winr_stop()" onmouseover="winr_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='winr_image'><video width=100% height=100% muted autoplay loop>
<source src="images/notre_160.mp4" type="video/mp4">
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<img src='images/notre.jpg' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://www.matthewtancik.com/learnit">
<papertitle>Learned Initializations for Optimizing Coordinate-Based Neural Representations</papertitle>
</a>
<br>
<a href="http://matthewtancik.com/">Matthew Tancik*</a>,
<a href="https://bmild.github.io/">Ben Mildenhall*</a>,
<a href="https://www.linkedin.com/in/terrance-wang/">Terrance Wang</a>,
<a href="https://www.linkedin.com/in/divi-schmidt-262044180/">Divi Schmidt</a>, <br>
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://www2.eecs.berkeley.edu/Faculty/Homepages/yirenng.html">Ren Ng</a>
<br>
<em>CVPR</em>, 2021   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="http://www.matthewtancik.com/learnit">project page</a> /
<a href="https://www.youtube.com/watch?v=A-r9itCzcyo">video</a> /
<a href="https://arxiv.org/abs/2012.02189">arXiv</a>
<p></p>
<p>Using meta-learning to find weight initializations for coordinate-based MLPs allows them to converge faster and generalize better.</p>
</td>
</tr>
<tr onmouseout="nerfw_stop()" onmouseover="nerfw_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nerfw_image'><video width=100% height=100% muted autoplay loop>
<source src="images/nerfw_after.mp4" type="video/mp4">
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<img src='images/nerfw_before.jpg' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://nerf-w.github.io/">
<papertitle>NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections</papertitle>
</a>
<br>
<a href="http://www.ricardomartinbrualla.com/">Ricardo Martin-Brualla*</a>,
<a href="https://scholar.google.com/citations?user=g98QcZUAAAAJ&hl=en">Noha Radwan*</a>,
<a href="https://research.google/people/105804/">Mehdi S. M. Sajjadi*</a>, <br>
<strong>Jonathan T. Barron</strong>,
<a href="https://scholar.google.com/citations?user=FXNJRDoAAAAJ&hl=en">Alexey Dosovitskiy</a>,
<a href="http://www.stronglyconvex.com/about.html">Daniel Duckworth</a>
<br>
<em>CVPR</em>, 2021   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://nerf-w.github.io/">project page</a> /
<a href="https://arxiv.org/abs/2008.02268">arXiv</a> /
<a href="https://www.youtube.com/watch?v=mRAKVQj5LRA">video</a>
<p></p>
<p>Letting NeRF reason about occluders and appearance variation produces photorealistic view synthesis using only unstructured internet photos.</p>
</td>
</tr>
<tr onmouseout="dualrefl_stop()" onmouseover="dualrefl_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='dualrefl_image'>
<img src='images/dualrefl_after.jpg' width="160"></div>
<img src='images/dualrefl_before.jpg' width="160">
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://sniklaus.com/dualref">
<papertitle>Learned Dual-View Reflection Removal</papertitle>
</a>
<br>
<a href="http://sniklaus.com/welcome">Simon Niklaus</a>,
<a href="https://people.eecs.berkeley.edu/~cecilia77/">Xuaner (Cecilia) Zhang</a>,
<strong>Jonathan T. Barron</strong>, <br>
<a href="http://nealwadhwa.com">Neal Wadhwa</a>,
<a href="http://rahuldotgarg.appspot.com/">Rahul Garg</a>,
<a href="http://web.cecs.pdx.edu/~fliu/">Feng Liu</a>,
<a href="https://people.csail.mit.edu/tfxue/">Tianfan Xue</a>
<br>
<em>WACV</em>, 2021
<br>
<a href="http://sniklaus.com/dualref">project page</a> /
<a href="https://arxiv.org/abs/2010.00702">arXiv</a>
<p></p>
<p>
Reflections and the things behind them often exhibit parallax, and this lets you remove reflections from stereo pairs.
</p>
</td>
</tr>
<tr onmouseout="nlt_stop()" onmouseover="nlt_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nlt_image'><video width=100% height=100% muted autoplay loop>
<source src="images/nlt_after.mp4" type="video/mp4">
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</video></div>
<img src='images/nlt_before.jpg' width="160">
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://nlt.csail.mit.edu/">
<papertitle>Neural Light Transport for Relighting and View Synthesis</papertitle>
</a>
<br>
<a href="http://people.csail.mit.edu/xiuming/">Xiuming Zhang</a>,
<a href="http://www.seanfanello.it/">Sean Fanello</a>,
<a href="https://research.google/people/105312/">Yun-Ta Tsai</a>,
<a href="http://kevinkingo.com/">Tiancheng Sun</a>,
<a href="https://people.csail.mit.edu/tfxue/">Tianfan Xue</a>,
<a href="https://research.google/people/106687/">Rohit Pandey</a>,
<a href="https://www.dtic.ua.es/~sorts/">Sergio Orts-Escolano</a>,
<a href="https://dl.acm.org/profile/99659224296">Philip Davidson</a>,
<a href="https://scholar.google.com/citations?user=5D0_pjcAAAAJ&hl=en">Christoph Rhemann</a>,
<a href="http://www.pauldebevec.com/">Paul Debevec</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://cseweb.ucsd.edu/~ravir/">Ravi Ramamoorthi</a>,
<a href="http://billf.mit.edu/">William T. Freeman</a>
<br>
<em>ACM TOG</em>, 2021
<br>
<a href="http://nlt.csail.mit.edu/">project page</a> /
<a href="https://arxiv.org/abs/2008.03806">arXiv</a> /
<a href="https://www.youtube.com/watch?v=OGEnCWZihHE">video</a>
<p></p>
<p>Embedding a convnet within a predefined texture atlas enables simultaneous view synthesis and relighting.</p>
</td>
</tr>
<tr onmouseout="lssr_stop()" onmouseover="lssr_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='lssr_image'>
<img src='images/lssr_after.jpg' width="160"></div>
<img src='images/lssr_before.jpg' width="160">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://cseweb.ucsd.edu/~viscomp/projects/SIGA20LightstageSuperres/">
<papertitle>Light Stage Super-Resolution: Continuous High-Frequency Relighting</papertitle>
</a>
<br>
<a href="http://kevinkingo.com/">Tiancheng Sun</a>,
<a href="https://cseweb.ucsd.edu/~zex014/">Zexiang Xu</a>
<a href="http://people.csail.mit.edu/xiuming/">Xiuming Zhang</a>,
<a href="http://www.seanfanello.it/">Sean Fanello</a>,
<a href="https://scholar.google.com/citations?user=5D0_pjcAAAAJ&hl=en">Christoph Rhemann</a>, <br>
<a href="https://www.pauldebevec.com/">Paul Debevec</a>,
<a href="https://research.google/people/105312/">Yun-Ta Tsai</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://cseweb.ucsd.edu/~ravir/">Ravi Ramamoorthi</a>
<br>
<em>SIGGRAPH Asia</em>, 2020
<br>
<a href="http://cseweb.ucsd.edu/~viscomp/projects/SIGA20LightstageSuperres/">project page</a> /
<a href="https://arxiv.org/abs/2010.08888">arXiv</a>
<p></p>
<p>
Scans for light stages are inherently aliased, but we can use learning to super-resolve them.
</p>
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<img src='images/lion_none.jpg' width="160">
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<a href="https://bmild.github.io/fourfeat/index.html">
<papertitle>Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains</papertitle>
</a>
<br>
<a href="http://matthewtancik.com/">Matthew Tancik*</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan*</a>,
<a href="https://bmild.github.io/">Ben Mildenhall*</a>,
<a href="https://people.eecs.berkeley.edu/~sfk/">Sara Fridovich-Keil</a>, <br>
<a href="https://www.linkedin.com/in/nithinraghavan">Nithin Raghavan</a>,
<a href="https://scholar.google.com/citations?user=lvA86MYAAAAJ&hl=en">Utkarsh Singhal</a>,
<a href="http://cseweb.ucsd.edu/~ravir/">Ravi Ramamoorthi</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://www2.eecs.berkeley.edu/Faculty/Homepages/yirenng.html">Ren Ng</a>
<br>
<em>NeurIPS</em>, 2020   <font color=#FF8080><strong>(Spotlight)</strong></font>
<br>
<a href="https://bmild.github.io/fourfeat/">project page</a> /
video: <a href="https://www.youtube.com/watch?v=nVA6K6Sn2S4">3 min</a>, <a href="https://www.youtube.com/watch?v=iKyIJ_EtSkw">10 min</a> /
<a href="https://arxiv.org/abs/2006.10739">arXiv</a> /
<a href="https://github.com/tancik/fourier-feature-networks">code</a>
<p></p>
<p>Composing neural networks with a simple Fourier feature mapping allows them to learn detailed high-frequency functions.</p>
</td>
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<a href="https://arxiv.org/abs/2007.07350">
<papertitle>A Generalization of Otsu's Method and Minimum Error Thresholding</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>
<br>
<em>ECCV</em>, 2020   <font color=#FF8080><strong>(Spotlight)</strong></font>
<br>
<a href="https://github.com/jonbarron/hist_thresh">code</a> /
<a href="https://www.youtube.com/watch?v=rHtQQlQo1Q4">video</a> /
<a href="data/BarronECCV2020.bib">bibtex</a>
<br>
<p></p>
<p>
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.
</p>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2006.04902">
<papertitle>What Matters in Unsupervised Optical Flow</papertitle>
</a>
<br>
<a href="http://ricojonschkowski.com/">Rico Jonschkowski</a>,
<a href="https://www.linkedin.com/in/austin-charles-stone-1ba33b138/">Austin Stone</a>,
<strong>Jonathan T. Barron</strong>, <br>
<a href="https://research.google/people/ArielGordon/">Ariel Gordon</a>,
<a href="https://www.linkedin.com/in/kurt-konolige/">Kurt Konolige</a>,
<a href="https://research.google/people/AneliaAngelova/">Anelia Angelova</a>
<br>
<em>ECCV</em>, 2020   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://github.com/google-research/google-research/tree/master/uflow">code</a>
<br>
<p></p>