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<!DOCTYPE HTML>
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Basudha Pal</title>
<meta name="author" content="Basudha Pal">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="shortcut icon" href="images/favicon/favicon.ico" type="image/x-icon">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
</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>
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<td style="padding:2.5%;width:63%;vertical-align:middle">
<p class="name" style="text-align: center;">
Basudha Pal
</p>
<p>I am a third year Ph.D. student in the <a href="https://engineering.jhu.edu/ece/">Department of Electrical and Computer Engineering</a> at <a href="https://engineering.jhu.edu/ece/">Johns Hopkins University, advised by Bloomberg Distinguished Professor <a href="https://engineering.jhu.edu/faculty/rama-chellappa/">Rama Chellappa</a>. My research interests center on exploring bias and enhancing interpretability within computer vision algorithms. Specifically, my work focuses on understanding and mitigating bias in deep generative models like diffusion models trained on sensitive datasets. Additionally, I have a keen interest in exploring data attribution-based algorithms for interpretability and debiasing purposes.
</p>
<p>During the Summer of 2024, I was a Computer Vision intern in the DSAI team at <a href="https://www.jnj.com/?&utm_source=google&utm_medium=cpc&utm_campaign=GO-USA-ENG-PS-Corporate+Equity-BC-PH-RN-BRAND_GENERAL&utm_content=J%26J+-+General&utm_term=j%26j&gad_source=1&gclid=CjwKCAjwg-24BhB_EiwA1ZOx8vrgE9CLt1FdY2WQfXlbN4SlU9O_u9MSdc0K9w8me8is73Px3jO7uRoCO04QAvD_BwE&gclsrc=aw.ds">Johnson and Johnson Innovative Medicine</a> mentored by <a href="https://scholar.google.com/citations?user=pUY2Kt4AAAAJ&hl=en">Dr. Brendon Lutnick</a> where I am continuing part-time for Fall 2024.
</p>
<p> Before joining Hopkins, I graduated from <a href= "https://www.manipal.edu/mu.html">Manipal Academy of Higher Education</a>, India, in 2022 with a Bachelor's degree in Electronics and Communication Engineering with a minor in Computational Intelligence. During my undergrad, I interned in the summer of 2021 at <a href= "https://www.etsmtl.ca/en/"> ETS Montreal</a> with the <a href="https://www.mitacs.ca/our-programs/globalink-research-internship-students/"> Mitacs Globalink Fellowship</a>.
<p style="text-align:center">
<a href="mailto:[email protected]">Email</a> /
<a href="data/BPal-CV.pdf">CV</a> /
<a href="https://www.linkedin.com/in/basudha-pal-587035217/">LinkedIn</a> /
<a href="https://scholar.google.com/citations?user=qyO_N_QAAAAJ&hl=en">Scholar</a> /
<a href="https://github.com/Bas-2k/">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%;object-fit: cover; border-radius: 50%;" alt="profile photo" src="images/JonBarron_circle.jpg" class="hoverZoomLink"></a>
</td>
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</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">
<h2>Research</h2>
<p>
I'm interested in building trustworthy AI systems by focussing on the causes of bias, developing bias mitigation algorithms and data attribution based algorithms for explainability.
</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>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src="images/eccv_2024.png" alt="gamma_face" width="160" height="100">
</td>
<td width="75%" valign="middle">
<a href="">
<papertitle>PSO-
NET: Development of an Automated Psoriasis Assessment System Using Attention-Based Interpretable Deep Neural
Networks</papertitle>
</a>
<br>
<a>Sharif Amit Kamran</a>, <a>Molly V Lucas</a>, <a>Brendon Lutnick</a>, <a>Chaitanya Parmar</a>, <strong>Basudha Pal</strong>, <a>Asha Patel Shah</a>, <a>David Apfel</a>, <a>Steven Fakharzade</a>, <a>Llyod Miller</a>, <a>Stephen Yip</a>, <a>Kristopher Standish</a>, <a>Gabriela Oana Cula</a>
<br>
<em>The 22nd IEEE International Symposium on Biomedical Imaging (ISBI)</em>, 2025 (Submitted)
<p> <a href="">Paper</a> | <a href="">Poster</a>
<p style="text-align:justify">Psoriasis is a chronic skin condition that requires ongoing treatment and assessment. While PASI (Psoriasis Area and Severity Index) is commonly used to measure severity, it has drawbacks, including the need for on-site clinic visits and time-consuming dermatologist ratings. We introduce PSO-Net, an interpretable model that analyzes images from various anatomical regions to produce attention-based scores and predict PASI. Our method includes a novel regression activation map for enhanced interpretability, achieving SoTA inter-class correlation scores against two clinician readers.
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src="images/eccv_2024.png" alt="gamma_face" width="160" height="100">
</td>
<td width="75%" valign="middle">
<a href="">
<papertitle>GAMMA-FACE: GAussian Mixture Models Amend Diffusion Models for Bias Mitigation in Face Images</papertitle>
</a>
<br>
<strong>Basudha Pal*</strong>, <a>Arunkumar Kannan*</a>, <a>Ram Prabhakar Kathirvel</a>, <a>Alice J. O'Toole</a>, <a>Rama Chellappa</a>
<br>
<em>ECCV</em>, 2024
<p> <a href="https://bas-2k.github.io/gamma-face/">Project Website</a> | <a href="data/ECCV_2024_Poster.pdf">Poster</a>
<p style="text-align:justify">GAMMA-FACE introduces a novel approach to address bias amplification in face-generative diffusion models using Gaussian Mixture Models (GMMs). By leveraging GMMs, we disentangle facial attributes and localize the means within the latent space of the diffusion model, effectively reducing bias on-the-fly without the need for retraining.</p>
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src="images/eccv_2024.png" alt="gamma_face" width="160" height="100">
</td>
<td width="75%" valign="middle">
<a href="">
<papertitle>DiversiNet: Mitigating Bias in Deep Classification Networks across Sensitive
Attributes through Diffusion-Generated Data</papertitle>
</a>
<br>
<strong>Basudha Pal*</strong>, <a>Aniket Roy</a>, <a>Ram Prabhakar Kathirvel</a>, <a>Alice J. O'Toole</a>, <a>Rama Chellappa</a>
<br>
<em>IJCB 2024 Special Session: Responsible AI for Biometrics</em>, 2024
<p> <a href="">Paper</a> | <a href="">Poster</a>
<p style="text-align:justify"> DiversiNet addresses demographic biases in deep learning models trained on limited datasets by augmenting underrepresented data with conditional diffusion-generated samples. Experiments show reduced bias and improved accuracy across sensitive target attributes in face images.
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src="images/eccv_2024.png" alt="gamma_face" width="160" height="100">
</td>
<td width="75%" valign="middle">
<a href="">
<papertitle>Localization of magnetocardiographic sources for myocardial infarction cases using deterministic and Bayesian approaches</papertitle>
</a>
<br>
<a>Vikas R Bhat</a>, <strong>Basudha Pal</strong>, <a>Anitha H</a>, <a>Ananthakrishna Thalengala</a>
<br>
<em>Scientific Reports, Nature</em>, 2022
<p> <a href="https://www.nature.com/articles/s41598-022-25919-3">Paper</a>
<p style="text-align:justify"> This project addresses inverse cardiac source localization using analytical and probabilistic methods, starting with Tikhonov regularization to estimate heart surface potentials from MCG signals. We introduce a Variational Bayesian inference approach for more accurate reconstructions, comparing it to traditional Bayesian methods and evaluating performance with RMSE and correlation metrics, including applications for myocardial infarction localization.
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src="images/eccv_2024.png" alt="gamma_face" width="160" height="100">
</td>
<td width="75%" valign="middle">
<a href="">
<papertitle>The SIUQRD and matern 5/2 GPR models describing the covid-19 pandemic in India</papertitle>
</a>
<br>
<strong>Basudha Pal</strong>, <a>Vikas R Bhat</a>, Anitha H
<br>
<em>IEEE ICECCT</em>, 2021
<p> <a href="https://ieeexplore.ieee.org/abstract/document/9616706">Paper</a>
<p style="text-align:justify"> This study presents an SIUQRD model for COVID-19 in India, estimating parameters for the first and second waves and predicting cases from June 19 to July 2, 2021, using Matern 5/2 GPR. The model’s predictions closely matched actual data, marking its first application for COVID-19 forecasting in India.
</td>
</tr>
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<img src='images/camp.png' width="160">
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://camp-nerf.github.io/">
<span class="papertitle">CamP: Camera Preconditioning for Neural Radiance Fields</span>
</a>
<br>
<a href="https://keunhong.com/">Keunhong Park</a>,
<a href="https://henzler.github.io/">Philipp Henzler</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://www.ricardomartinbrualla.com/">Ricardo Martin-Brualla</a>
<br>
<em>SIGGRAPH Asia</em>, 2023
<br>
<a href="https://camp-nerf.github.io/">project page</a>
/
<a href="https://arxiv.org/abs/2308.10902">arXiv</a>
<p></p>
<p>
Preconditioning based on camera parameterization helps NeRF and camera extrinsics/intrinsics optimize better together.
</p>
</td>
</tr>
-->
<!--
<tr onmouseout="zipnerf_stop()" onmouseover="zipnerf_start()" bgcolor="#ffffd0">
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<source src="images/zipnerf.mp4" type="video/mp4">
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<img src='images/zipnerf.jpg' width="160">
</div>
<script type="text/javascript">
function zipnerf_start() {
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://jonbarron.info/zipnerf">
<span class="papertitle">Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields</span>
</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>ICCV</em>, 2023   <font color="red"><strong>(Oral Presentation, Best Paper Finalist)</strong></font>
<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()">
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<div class="one">
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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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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://dreambooth3d.github.io/">
<span class="papertitle">DreamBooth3D: Subject-Driven Text-to-3D Generation</span>
</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>ICCV</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="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">
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</video></div>
<img src='images/bakedsdf_before.jpg' width="160">
</div>
<script type="text/javascript">
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bakedsdf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://bakedsdf.github.io/">
<span class="papertitle">BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis</span>
</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">
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<img src='images/merf_before.jpg' width="160">
</div>
<script type="text/javascript">
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merf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://merf42.github.io/">
<span class="papertitle">MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes</span>
</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">
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</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">
<span class="papertitle">Eclipse: Disambiguating Illumination and Materials using Unintended Shadows</span>
</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="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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alignerf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://yifanjiang19.github.io/alignerf">
<span class="papertitle">AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training</span>
</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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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://dreamfusion3d.github.io/">
<span class="papertitle">DreamFusion: Text-to-3D using 2D Diffusion</span>
</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="guandao_stop()" onmouseover="guandao_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='guandao_image'>
<img src='images/guandao_after.png' width="160"></div>
<img src='images/guandao_before.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://arxiv.org/abs/2304.14473">
<span class="papertitle">Learning a Diffusion Prior for NeRFs</span>
</a>
<br>
<a href="https://www.guandaoyang.com/">Guandao Yang</a>,
<a href="https://abhijitkundu.info/">Abhijit Kundu</a>,
<a href="https://geometry.stanford.edu/member/guibas/index.html">Leonidas J. Guibas</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://cs.stanford.edu/~poole/">Ben Poole</a>
<br>
<em>ICLR Workshop</em>, 2023
<p></p>
<p>
Training a diffusion model on grid-based NeRFs lets you (conditionally) sample NeRFs.
</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">
<span class="papertitle">MIRA: Mental Imagery for Robotic Affordances</span>
</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/">
<span class="papertitle">SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image Collections</span>
</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">
<span class="papertitle">Polynomial Neural Fields for Subband Decomposition</span>
</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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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://yifanjiang.net/MalleConv.html">
<span class="papertitle">Fast and High-Quality Image Denoising via Malleable Convolutions</span>
</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">
Your browser does not support the video tag.
</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/">
<span class="papertitle">NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields</span>
</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">
Your browser does not support the video tag.
</video></div>
<img src='images/refnerf.jpg' width="160">
</div>
<script type="text/javascript">
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refnerf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://dorverbin.github.io/refnerf/index.html">
<span class="papertitle">Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields</span>
</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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</video></div>
<img src='images/mip360_sat.jpg' width="160">
</div>
<script type="text/javascript">
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}
mip360_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://jonbarron.info/mipnerf360">
<span class="papertitle">Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields</span>
</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>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://bmild.github.io/rawnerf/index.html">
<span class="papertitle">NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images</span>
</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>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://m-niemeyer.github.io/regnerf/index.html">
<span class="papertitle">RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs</span>
</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>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://waymo.com/research/block-nerf/">
<span class="papertitle">Block-NeRF: Scalable Large Scene Neural View Synthesis</span>
</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>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://grail.cs.washington.edu/projects/humannerf/">
<span class="papertitle">HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video</span>
</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>