Sayan Nath

Grad. Research Assistant

A portrait of Sayan Nath

sayannath.github.io/

sayannath

sayannath235

sayannath235@gmail.com

Education

University of Washington
Ph. D in Computer Science, advised by Steven M. Seitz and Ali Farhadi.
Supported by Samsung Scholarship 2015-2020 ($50,000/year for 5 years)
Fall 2015 - Spring 2022
University of Illinois at Urbana-Champaign
B.S. in Computer Science, advised by Derek Hoiem.
Fall 2009 - Spring 2013

Employment

Google
Research Scientist
May 2022 - Jan 2024
San Francisco, CA
Google
Research Intern on the Project Starline team. Worked on Nerfies and HyperNeRF.
Jun 2020 - Sep 2021
Seattle, WA
NVIDIA
Robotics Research Intern at the Seattle Robotics Lab. Worked on LatentFusion.
Jul 2019 - Nov 2019
Seattle, WA
Amazon
Applied Scientist Intern on the Amazon Go team. Worked on human activity detection.
Jul 2017 - Sep 2017
Seattle, WA
Ministry of National Defense, Cyber Command
Software Engineer (mandatory military service). Worked on network monitoring software.
Oct 2013 - Jul 2015
Seoul, Korea
Google
Software Engineering Intern. Create document conversion system for Google Cloud Print.
May 2013 - Aug 2013
Mountain View, CA
Qualcomm
Software Engineering Intern. Optimized performance of JGit, reducing push times from hours to seconds. Implemented multi-master support for Gerrit Code Review.
May 2012 - Aug 2012
Boulder, CO

Publications

IllumiNeRF 3D Relighting without Inverse Rendering
X. Zhao, P. Srinivasan, D. Verbin, K. Park, R. Martin-Brualla, P. Henzler
3D relighting by distilling samples from a 2D image relighting diffusion model into a latent-variable NeRF.
NeurIPS, 2024
ReconFusion: 3D Reconstruction with Diffusion Priors
R. Wu, B. Mildenhall, P. Henzler, K. Park, R. Gao, D. Watson, P. Srinivasan, D. Verbin, J. Barron, B. Poole, A. Holynski
Using an multi-view image conditioned diffusion model to regularize a NeRF enabled few-view reconstruction.
CVPR, 2024
CamP: Camera Preconditioning for Neural Radiance Fields
K. Park, P. Henzler, B. Mildenhall, J. Barron, R. Martin-Brualla
Preconditioning camera optimization during NeRF training significantly improves their ability to jointly recover the scene and camera parameters.
SIGGRAPH Asia, 2023
HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields
K. Park, U. Sinha, P. Hedman, J. Barron, S. Bouaziz, D. Goldman, R. Martin-Brualla, S. Seitz
By applying ideas from level set methods, we can represent topologically changing scenes with NeRFs.
SIGGRAPH Asia, 2021
FiG-NeRF: Figure Ground Neural Radiance Fields for 3D Object Category Modelling
C. Xie, K. Park, R. Martin-Brualla, M. Brown
Given a lot of images of an object category, you can train a NeRF to render them from novel views and interpolate between different instances.
3DV, 2021
Nerfies: Deformable Neural Radiance Fields
K. Park, U. Sinha, J. Barron, S. Bouaziz, D. Goldman, S. Seitz, R. Martin-Brualla
Learning deformation fields with a NeRF let's you reconstruct non-rigid scenes with high fidelity.
ICCV, 2021
LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation
K. Park, A. Mousavian, Y. Xiang, D. Fox
By learning to predict geometry from images, you can do zero-shot pose estimation with a single network.
CVPR, 2020
PhotoShape: Photorealistic Materials for Large-Scale Shape Collections
K. Park, K. Rematas, A. Farhadi, S. Seitz
By pairing large collections of images, 3D models, and materials, you can create thousands of photorealistic 3D models fully automatically.
SIGGRAPH Asia, 2018