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.

Contact: gene@cs.cornell.edu

cv / github / linkedin / google scholar / pictures of my cat

Research

	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 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 / teaser video 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.

Website template borrowed from Jon Barron.