Gene Chou

I am a first year CS PhD student at Cornell University, advised by Noah Snavely and Bharath Hariharan. Previously, I received my bachelor's in computer science and math from Princeton University and worked with Felix Heide. My research focuses on 3D reconstruction and generation. I am supported by the NSF graduate fellowship.

Contact: gene@cs.cornell.edu

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Research


MegaScenes: Scene-Level View Synthesis at Scale
Joseph Tung*, Gene Chou*, Ruojin Cai, Guandao Yang, Kai Zhang, Gordon Wetzstein, Bharath Hariharan, Noah Snavely
Arxiv 2024
paper / code / project page

MegaScenes is a scene-level dataset containing 100K SfM reconstructions and 2M registered images, collected from Wikimedia Commons. We validate its effectiveness in training large-scale, generalizable models on the task of single image novel view synthesis.

YOLOR-Based Multi-Task Learning
Hung-Shuo Chang, Chien-Yao Wang, Richard Wang, Gene Chou, Hong-Yuan Mark Liao
Arxiv 2023
paper

Builds on YOLOR to jointly train vision (e.g. object detection, instance and semantic segmentation) and vision-language (e.g. image captioning) tasks. Fast and lightweight while achieving competitive performance.

Thin On-Sensor Nanophotonic Array Cameras
Praneeth Chakravarthula, Jipeng Sun, Xiao Li, Chenyang Lei, Gene Chou, Mario Bijelic, Johannes Froesch, Arka Majumdar, Felix Heide
SIGGRAPH ASIA 2023
paper / project page

Recovers images in broadband using a single flat metasurface optic. Compensates for residual aberrations with probabilistic deconvolution implemented using a conditional diffusion model.

Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functions
Gene Chou, Yuval Bahat, Felix Heide
ICCV 2023
paper / code / project page

Performs diffusion on the latent space of neural SDFs while providing geometric guidance. Generates diverse meshes conditioned on partial point clouds, 2D images, and real-scanned, noisy point clouds.

GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions
Gene Chou, Ilya Chugunov, Felix Heide
NeurIPS 2022
paper / code / project page

Combines a semi-supervised approach with a self-supervised loss to reconstruct neural SDFs from raw input point clouds of over a hundred unseen object classes.


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