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<!DOCTYPE HTML>
<html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Baotong Tian</title>
<meta name="author" content="Baotong Tian">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
<!-- <link rel="icon" href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.9em%22 font-size=%2290%22>🌐</text></svg>"> -->
</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><p style="font-weight: bold;">Note: This website is my old homepage, for more recent information please refer to <a href="https://user-tian.github.io">here.</a></p></p>
<p style="text-align:center">
<name>Baotong Tian</name>
</p>
<p>I am a senior undergraduate student in the Department of Automation at Tsinghua University. I will be receiving my Bachelor's degree from the Department of Automation in 2024. I’m also a member of the <a href="https://tongclass.ac.cn/author/baotong-tian/">Tong(General Artificial Intelligence) class</a>. Currently, I'm working with Prof.<a href="http://ivg.au.tsinghua.edu.cn/Jiwen_Lu/">Jiwen Lu</a>.
</p>
<p>My research interests are in MIR(music technology), particularly incorporating AI in music information retrieval tasks and controllable music generation. I also have some interest in deep learning theory. I'm currently working in <a href="http://ivg.au.tsinghua.edu.cn/"> i-VisionGroup </a> on explainable AI.
</p>
<p style="text-align:center">
<a href="[email protected]">Email</a>  / 
<a href="mydata/CV_Baotong Tian.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/User-tian">Github</a>
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<a href="my images/Baotong.jpg"><img style="width:100%;max-width:100%" alt="profile photo" src="my images/Baotong_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>News</heading>
<li style="margin: 5px;">
[2023.09] Our work on simple and fast Shapley value estimation was submitted to <b>ICLR 2024</b>!
</li>
<!-- <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="bakedsdf_stop()" onmouseover="bakedsdf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='bakedsdf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/bakedsdf_after.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/bakedsdf_before.jpg' width="160">
</div>
<script type="text/javascript">
function bakedsdf_start() {
document.getElementById('bakedsdf_image').style.opacity = "1";
}
function bakedsdf_stop() {
document.getElementById('bakedsdf_image').style.opacity = "0";
}
bakedsdf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://bakedsdf.github.io/">
<papertitle>BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis</papertitle>
</a>
<br>
<a href="https://lioryariv.github.io/">Lior Yariv*</a>,
<a href="https://phogzone.com/">Peter Hedman*</a>,
<a href="https://creiser.github.io/">Christian Reiser</a>,
<a href="https://dorverbin.github.io/">Dor Verbin</a>, <br>
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<a href="https://szeliski.org/RichardSzeliski.htm">Richard Szeliski</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>
<br>
<em>SIGGRAPH</em>, 2023
<br>
<a href="https://bakedsdf.github.io/">project page</a>
/
<a href="https://www.youtube.com/watch?v=fThKXZ6uDTk">video</a>
/
<a href="https://arxiv.org/abs/2302.14859">arXiv</a>
<p></p>
<p>
We use SDFs to bake a NeRF-like model into a high quality mesh and do real-time view synthesis.
</p>
</td>
</tr>
<tr onmouseout="merf_stop()" onmouseover="merf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='merf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/merf_after.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/merf_before.jpg' width="160">
</div>
<script type="text/javascript">
function merf_start() {
document.getElementById('merf_image').style.opacity = "1";
}
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}
merf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://merf42.github.io/">
<papertitle>MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes</papertitle>
</a>
<br>
<a href="https://creiser.github.io/">Christian Reiser</a>,
<a href="https://szeliski.org/RichardSzeliski.htm">Richard Szeliski</a>,
<a href="https://dorverbin.github.io/">Dor Verbin</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>, <br>
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://www.cvlibs.net/">Andreas Geiger</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://phogzone.com/">Peter Hedman</a>
<br>
<em>SIGGRAPH</em>, 2023
<br>
<a href="https://merf42.github.io/">project page</a>
/
<a href="https://www.youtube.com/watch?v=3EACM2JAcxc">video</a>
/
<a href="https://arxiv.org/abs/2302.12249">arXiv</a>
<p></p>
<p>
We use volumetric rendering with a sparse 3D feature grid and 2D feature planes to do real-time view synthesis.
</p>
</td>
</tr>
<tr onmouseout="eclipse_stop()" onmouseover="eclipse_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='eclipse_image'><video width=100% height=100% muted autoplay loop>
<source src="images/eclipse_after.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/eclipse_before.jpg' width="160">
</div>
<script type="text/javascript">
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eclipse_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://dorverbin.github.io/eclipse">
<papertitle>Eclipse: Disambiguating Illumination and Materials using Unintended Shadows</papertitle>
</a>
<br>
<a href="https://dorverbin.github.io/">Dor Verbin</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://phogzone.com/">Peter Hedman</a>, <br>
<strong>Jonathan T. Barron</strong>,
<a href="Todd Zickler">Todd Zickler</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>
<br>
<em>arXiv</em>, 2023
<br>
<a href="https://dorverbin.github.io/eclipse">project page</a>
/
<a href="https://www.youtube.com/watch?v=amQLGyza3EU">video</a>
/
<a href="https://arxiv.org/abs/2305.16321">arXiv</a>
<p></p>
<p>
Shadows cast by unobserved occluders provide a high-frequency cue for recovering illumination and materials.
</p>
</td>
</tr>
<tr onmouseout="zipnerf_stop()" onmouseover="zipnerf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='zipnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/zipnerf.mp4" type="video/mp4">
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<img src='images/zipnerf.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://jonbarron.info/zipnerf">
<papertitle>Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://scholar.harvard.edu/dorverbin/home">Dor Verbin</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<a href="https://phogzone.com/">Peter Hedman</a>
<br>
<em>arXiv</em>, 2023
<br>
<a href="http://jonbarron.info/zipnerf">project page</a>
/
<a href="https://www.youtube.com/watch?v=xrrhynRzC8k">video</a>
/
<a href="https://arxiv.org/abs/2304.06706">arXiv</a>
<p></p>
<p>
Combining mip-NeRF 360 and grid-based models like Instant NGP lets us reduce error rates by 8%–77% and accelerate training by 24x.
</p>
</td>
</tr>
<tr onmouseout="db3d_stop()" onmouseover="db3d_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='db3d_image'><video width=100% height=100% muted autoplay loop>
<source src="images/owl.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/owl.png' width="160">
</div>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://dreambooth3d.github.io/">
<papertitle>DreamBooth3D: Subject-Driven Text-to-3D Generation</papertitle>
</a>
<br>
<a href="https://amitraj93.github.io/">Amit Raj</a>, <a href="https://www.linkedin.com/in/srinivas-kaza-64223b74">Srinivas Kaza</a>, <a href="https://poolio.github.io/">Ben Poole</a>, <a href="https://m-niemeyer.github.io/">Michael Niemeyer</a>, <a href="https://natanielruiz.github.io/">Nataniel Ruiz</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>, <a href="https://scholar.google.com/citations?user=I2qheksAAAAJ">Shiran Zada</a>, <a href="https://kfiraberman.github.io/">Kfir Aberman</a>, <a href="http://people.csail.mit.edu/mrub/">Michael Rubinstein</a>,
<strong>Jonathan T. Barron</strong>, <a href="http://people.csail.mit.edu/yzli/">Yuanzhen Li</a>, <a href="https://varunjampani.github.io/">Varun Jampani</a>
<br>
<em>arXiv</em>, 2023
<br>
<a href="https://dreambooth3d.github.io/">project page</a> /
<a href="https://arxiv.org/abs/2303.13508">arXiv</a>
<p></p>
<p>Combining DreamBooth (personalized text-to-image) and DreamFusion (text-to-3D) yields high-quality, subject-specific 3D assets with text-driven modifications</p>
</td>
</tr>
<tr onmouseout="alignerf_stop()" onmouseover="alignerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='alignerf_image'>
<img src='images/alignerf_after.jpg' width="160"></div>
<img src='images/alignerf_before.jpg' width="160">
</div>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://yifanjiang19.github.io/alignerf">
<papertitle>AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training
</papertitle>
</a>
<br>
<a href="https://yifanjiang.net/">Yifan Jiang</a>,
<a href="https://phogzone.com/">Peter Hedman</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://ir1d.github.io/">Dejia Xu</a>, <br>
<strong>Jonathan T. Barron</strong>,
<a href="https://spark.adobe.com/page/CAdrFMJ9QeI2y/">Zhangyang Wang</a>,
<a href="https://tianfan.info/">Tianfan Xue</a>
<br>
<em>CVPR</em>, 2023
<br>
<a href="https://yifanjiang19.github.io/alignerf">project page</a>
/
<a href="https://arxiv.org/abs/2211.09682">arXiv</a>
<p></p>
<p>
Accounting for misalignment due to scene motion or calibration errors improves NeRF reconstruction quality.
</p>
</td>
</tr>
<tr onmouseout="dreamfusion_stop()" onmouseover="dreamfusion_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='dreamfusion_image'><video width=100% height=100% muted autoplay loop>
<source src="images/dreamfusion.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/dreamfusion.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://dreamfusion3d.github.io/">
<papertitle>DreamFusion: Text-to-3D using 2D Diffusion</papertitle>
</a>
<br>
<a href="https://cs.stanford.edu/~poole/">Ben Poole</a>,
<a href="https://www.ajayj.com/">Ajay Jain</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>
<br>
<em>ICLR</em>, 2023   <font color="red"><strong>(Oral Presentation, Outstanding Paper Award)</strong></font>
<br>
<a href="https://dreamfusion3d.github.io/">project page</a>
/
<a href="https://arxiv.org/abs/2209.14988">arXiv</a>
/
<a href="https://dreamfusion3d.github.io/gallery.html">gallery</a>
<p></p>
<p>
We optimize a NeRF from scratch using a pretrained text-to-image diffusion model to do text-to-3D generative modeling.
</p>
</td>
</tr>
<tr onmouseout="mira_stop()" onmouseover="mira_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='mira_image'>
<img src='images/mira_after.jpg' width="160"></div>
<img src='images/mira_before.jpg' width="160">
</div>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://openreview.net/forum?id=AmPeAFzU3a4">
<papertitle>MIRA: Mental Imagery for Robotic Affordances</papertitle>
</a>
<br>
<a href="https://yenchenlin.me/">Lin Yen-Chen</a>,
<a href="http://www.peteflorence.com/">Pete Florence</a>,
<a href="https://andyzeng.github.io/">Andy Zeng</a>, <strong>Jonathan T. Barron</strong>,
<a href="https://yilundu.github.io/">Yilun Du</a>,
<a href="https://people.csail.mit.edu/weichium/">Wei-Chiu Ma</a>,
<a href="https://anthonysimeonov.github.io/">Anthony Simeonov</a>,
<a href="https://meche.mit.edu/people/faculty/[email protected]">Alberto Rodriguez</a>,
<a href="http://web.mit.edu/phillipi/">Phillip Isola</a>
<br>
<em>CoRL</em>, 2022
<p></p>
<p>
NeRF lets us synthesize novel orthographic views that work well with pixel-wise algorithms for robotic manipulation.
</p>
</td>
</tr>
<tr onmouseout="samurai_stop()" onmouseover="samurai_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='samurai_image'>
<img src='images/samurai_after.jpg' width="160"></div>
<img src='images/samurai_before.jpg' width="160">
</div>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://markboss.me/publication/2022-samurai/">
<papertitle>SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image Collections</papertitle>
</a>
<br>
<a href="https://markboss.me">Mark Boss</a>,
<a href="">Andreas Engelhardt</a>,
<a href="https://abhishekkar.info/">Abhishek Kar</a>,
<a href="http://people.csail.mit.edu/yzli/">Yuanzhen Li</a>,
<a href="https://deqings.github.io/">Deqing Sun</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>,
<a href="https://varunjampani.github.io">Varun Jampani</a>
<br>
<em>NeurIPS</em>, 2022
<br>
<a href="https://markboss.me/publication/2022-samurai/">project page</a> /
<a href="https://www.youtube.com/watch?v=LlYuGDjXp-8">video</a> /
<a href="https://arxiv.org/abs/2205.15768">arXiv</a>
<p></p>
<p>
A joint optimization framework for estimating shape, BRDF, camera pose, and illumination from in-the-wild image collections.
</p>
</td>
</tr>
<tr onmouseout="pnf_stop()" onmouseover="pnf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='pnf_image'>
<img src='images/pnf_before.jpg' width="160"></div>
<img src='images/pnf_after.jpg' width="160">
</div>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="TODO">
<papertitle>Polynomial Neural Fields for Subband Decomposition</papertitle>
</a> <br>
<a href="https://www.guandaoyang.com/">Guandao Yang*</a>,
<a href="https://sagiebenaim.github.io/">Sagie Benaim*</a>,
<a href="https://varunjampani.github.io/">Varun Jampani</a>,
<a href="https://www.kylegenova.com/">Kyle Genova</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://www.cs.princeton.edu/~funk/">Thomas Funkhouser</a>,
<a href="http://home.bharathh.info/">Bharath Hariharan</a>,
<a href="https://sergebelongie.github.io/">Serge Belongie</a>
<br>
<em>NeurIPS</em>, 2022
<p>
Representing neural fields as a composition of manipulable and interpretable components lets you do things like reason about frequencies and scale.
</p>
</td>
</tr>
<tr onmouseout="malle_stop()" onmouseover="malle_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='malle_image'>
<img src='images/MalleConv_after.jpg' width="160"></div>
<img src='images/MalleConv_before.jpg' width="160">
</div>
<script type="text/javascript">
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malle_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://yifanjiang.net/MalleConv.html">
<papertitle>Fast and High-Quality Image Denoising via Malleable Convolutions</papertitle>
</a>
<br>
<a href="https://yifanjiang.net/">Yifan Jiang</a>,
<a href="https://bartwronski.com/">Bartlomiej Wronski</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>, <br>
<strong>Jonathan T. Barron</strong>,
<a href="https://spark.adobe.com/page/CAdrFMJ9QeI2y/">Zhangyang Wang</a>,
<a href="https://tianfan.info/">Tianfan Xue</a>
<br>
<em>ECCV</em>, 2022
<br>
<a href="https://yifanjiang.net/MalleConv.html">project page</a>
/
<a href="https://arxiv.org/abs/2201.00392">arXiv</a>
<p></p>
<p>
We denoise images efficiently by predicting spatially-varying kernels at low resolution and using a fast fused op to jointly upsample and apply these kernels at full resolution.
</p>
</td>
</tr>
<tr onmouseout="nerfsuper_stop()" onmouseover="nerfsuper_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nerfsuper_image'><video width=100% height=100% muted autoplay loop>
<source src="images/nerf_supervision.mp4" type="video/mp4">
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</video></div>
<img src='images/nerf_supervision.jpg' width="160">
</div>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://yenchenlin.me/nerf-supervision/">
<papertitle>NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields</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://scholar.google.com/citations?user=_BPdgV0AAAAJ&hl=en">Tsung-Yi Lin</a>,
<a href="https://meche.mit.edu/people/faculty/[email protected]">Alberto Rodriguez</a>,
<a href="http://web.mit.edu/phillipi/">Phillip Isola</a>
<br>
<em>ICRA</em>, 2022
<br>
<a href="http://yenchenlin.me/nerf-supervision/">project page</a> /
<a href="https://arxiv.org/abs/2203.01913">arXiv</a> /
<a href="https://www.youtube.com/watch?v=_zN-wVwPH1s">video</a> /
<a href="https://github.com/yenchenlin/nerf-supervision-public">code</a> /
<a href="https://colab.research.google.com/drive/13ISri5KD2XeEtsFs25hmZtKhxoDywB5y?usp=sharing">colab</a>
<p></p>
<p>NeRF works better than RGB-D cameras or multi-view stereo when learning object descriptors.</p>
</td>
</tr>
<tr onmouseout="refnerf_stop()" onmouseover="refnerf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='refnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/refnerf.mp4" type="video/mp4">
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</video></div>
<img src='images/refnerf.jpg' width="160">
</div>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://dorverbin.github.io/refnerf/index.html">
<papertitle>Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields</papertitle>
</a>
<br>
<a href="https://scholar.harvard.edu/dorverbin/home">Dor Verbin</a>,
<a href="https://phogzone.com/">Peter Hedman</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>, <br>
<a href="Todd Zickler">Todd Zickler</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>
<br>
<em>CVPR</em>, 2022   <font color="red"><strong>(Oral Presentation, Best Student Paper Honorable Mention)</strong></font>
<br>
<a href="https://dorverbin.github.io/refnerf/index.html">project page</a>
/
<a href="https://arxiv.org/abs/2112.03907">arXiv</a>
/
<a href="https://youtu.be/qrdRH9irAlk">video</a>
<p></p>
<p>Explicitly modeling reflections in NeRF produces realistic shiny surfaces and accurate surface normals, and lets you edit materials.</p>
</td>
</tr>
<tr onmouseout="mip360_stop()" onmouseover="mip360_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='mip360_image'><video width=100% height=100% muted autoplay loop>
<source src="images/mip360_sat.mp4" type="video/mp4">
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<img src='images/mip360_sat.jpg' width="160">
</div>
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://jonbarron.info/mipnerf360">
<papertitle>Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://scholar.harvard.edu/dorverbin/home">Dor Verbin</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<a href="https://phogzone.com/">Peter Hedman</a>
<br>
<em>CVPR</em>, 2022   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="http://jonbarron.info/mipnerf360">project page</a>
/
<a href="https://arxiv.org/abs/2111.12077">arXiv</a>
/
<a href="https://youtu.be/zBSH-k9GbV4">video</a>
<p></p>
<p>mip-NeRF can be extended to produce realistic results on unbounded scenes.</p>
</td>
</tr>
<tr onmouseout="rawnerf_stop()" onmouseover="rawnerf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='rawnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/rawnerf.mp4" type="video/mp4">
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</video></div>
<img src='images/rawnerf.jpg' width="160">
</div>
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://bmild.github.io/rawnerf/index.html">
<papertitle>NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images</papertitle>
</a>
<br>
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://phogzone.com/">Peter Hedman</a>,
<a href="http://www.ricardomartinbrualla.com/">Ricardo Martin-Brualla</a>, <br>
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>CVPR</em>, 2022   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://bmild.github.io/rawnerf/index.html">project page</a>
/
<a href="https://arxiv.org/abs/2111.13679">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=JtBS4KBcKVc">video</a>
<p></p>
<p>
Properly training NeRF on raw camera data enables HDR view synthesis and bokeh, and outperforms multi-image denoising.</p>
</td>
</tr>
<tr onmouseout="regnerf_stop()" onmouseover="regnerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='regnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/regnerf_after.mp4" type="video/mp4">
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</video></div>
<img src='images/regnerf_before.jpeg' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://m-niemeyer.github.io/regnerf/index.html">
<papertitle>RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs</papertitle>
</a>
<br>
<a href="https://m-niemeyer.github.io/">Michael Niemeyer</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>, <br>
<a href="https://msmsajjadi.github.io/">Mehdi S. M. Sajjadi</a>,
<a href="http://www.cvlibs.net/">Andreas Geiger</a>,
<a href="http://www2.informatik.uni-freiburg.de/~radwann/">Noha Radwan</a>
<br>
<em>CVPR</em>, 2022   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://m-niemeyer.github.io/regnerf/index.html">project page</a>
/
<a href="https://arxiv.org/abs/2112.00724">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=QyyyvA4-Kwc">video</a>
<p></p>
<p>Regularizing unseen views during optimization enables view synthesis from as few as 3 input images.</p>
</td>
</tr>
<tr onmouseout="blocknerf_stop()" onmouseover="blocknerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='blocknerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/blocknerf_after.mp4" type="video/mp4">
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<img src='images/blocknerf_before.jpg' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://waymo.com/research/block-nerf/">
<papertitle>Block-NeRF: Scalable Large Scene Neural View Synthesis</papertitle>
</a>
<br>
<a href="http://matthewtancik.com/">Matthew Tancik</a>,
<a href="http://casser.io/">Vincent Casser</a>,
<a href="https://sites.google.com/site/skywalkeryxc/">Xinchen Yan</a>,
<a href="https://scholar.google.com/citations?user=5mJUkI4AAAAJ&hl=en">Sabeek Pradhan</a>, <br>
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://www.henrikkretzschmar.com/">Henrik Kretzschmar</a>
<br>
<em>CVPR</em>, 2022   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://waymo.com/research/block-nerf/">project page</a>
/
<a href="https://arxiv.org/abs/2202.05263">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=6lGMCAzBzOQ">video</a>
<p></p>
<p>We can do city-scale reconstruction by training multiple NeRFs with millions of images.</p>
</td>
</tr>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='hnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/hnerf_after.mp4" type="video/mp4">
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</video></div>
<img src='images/hnerf_before.jpg' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://grail.cs.washington.edu/projects/humannerf/">
<papertitle>HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video</papertitle>
</a>
<br>
<a href="https://homes.cs.washington.edu/~chungyi/">Chung-Yi Weng</a>,
<a href="https://homes.cs.washington.edu/~curless/">Brian Curless</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>, <br>
<strong>Jonathan T. Barron</strong>,
<a href="https://www.irakemelmacher.com/">Ira Kemelmacher-Shlizerman </a>
<br>
<em>CVPR</em>, 2022   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://grail.cs.washington.edu/projects/humannerf/">project page</a>
/
<a href="https://arxiv.org/abs/2201.04127">arXiv</a>
/
<a href="https://youtu.be/GM-RoZEymmw">video</a>
<p></p>
<p>Combining NeRF with pose estimation lets you use a monocular video to do free-viewpoint rendering of a human.</p>
</td>
</tr>
<tr onmouseout="urf_stop()" onmouseover="urf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='urf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/urf.mp4" type="video/mp4">
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<img src='images/urf.jpg' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://urban-radiance-fields.github.io/">
<papertitle>Urban Radiance Fields</papertitle>
</a>
<br>
<a href="http://www.krematas.com/">Konstantinos Rematas</a>,
<a href="https://andrewhliu.github.io/">Andrew Liu</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul P. Srinivasan</a>,
<strong>Jonathan T. Barron</strong>, <br>
<a href="https://taiya.github.io/">Andrea Tagliasacchi</a>,
<a href="https://www.cs.princeton.edu/~funk/">Tom Funkhouser</a>,
<a href="https://sites.google.com/corp/view/vittoferrari"> Vittorio Ferrari</a>
<br>
<em>CVPR</em>, 2022
<br>
<a href="https://urban-radiance-fields.github.io/">project page</a>
/
<a href="https://arxiv.org/abs/2111.14643">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=qGlq5DZT6uc">video</a>
<p></p>
<p>
Incorporating lidar and explicitly modeling the sky lets you reconstruct urban environments.</p>
</td>
</tr>
<tr onmouseout="ddp_stop()" onmouseover="ddp_start()">
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<a href="https://arxiv.org/abs/2112.03288">
<papertitle>Dense Depth Priors for Neural Radiance Fields from Sparse Input Views</papertitle>
</a>
<br>
<a href="https://niessnerlab.org/members/barbara_roessle/profile.html">Barbara Roessle</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<a href="https://www.niessnerlab.org/">Matthias Nießner</a>
<br>
<em>CVPR</em>, 2022
<br>
<a href="https://arxiv.org/abs/2112.03288">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=zzkvvdcvksc">video</a>
<p></p>
<p>
Dense depth completion techniques applied to freely-available sparse stereo data can improve NeRF reconstructions in low-data regimes.
</p>
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<a href="https://ajayj.com/dreamfields">
<papertitle>Zero-Shot Text-Guided Object Generation with Dream Fields</papertitle>
</a>
<br>
<a href="https://www.ajayj.com/">Ajay Jain</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://people.eecs.berkeley.edu/~pabbeel/">Pieter Abbeel</a>,
<a href="https://cs.stanford.edu/~poole/">Ben Poole</a>
<br>
<em>CVPR</em>, 2022
<br>
<a href="https://ajayj.com/dreamfields">project page</a>
/
<a href="https://arxiv.org/abs/2112.01455">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=1Fke6w46tv4">video</a>
<p></p>
<p>Supervising the CLIP embeddings of NeRF renderings lets you to generate 3D objects from text prompts.</p>
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