most citedNeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild

52 citations · 59 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV202152 cited

NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild

Jason Y. Zhang, Gengshan Yang, Shubham Tulsiani +1

Recent history has seen a tremendous growth of work exploring implicit representations of geometry and radiance, popularized through Neural Radiance Fields (NeRF). Such works are f…

cs.CV2021

LASR: Learning Articulated Shape Reconstruction from a Monocular Video

Gengshan Yang, Deqing Sun, Varun Jampani +6

Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. However, it is still challenging to reconstruct nonrigid structur…

cs.CV2021

Learning to Segment Rigid Motions from Two Frames

Gengshan Yang, Deva Ramanan

Appearance-based detectors achieve remarkable performance on common scenes, but tend to fail for scenarios lack of training data. Geometric motion segmentation algorithms, however,…

cs.CV20195 cited

Hierarchical Deep Stereo Matching on High-resolution Images

Gengshan Yang, Joshua Manela, Michael Happold +1

We explore the problem of real-time stereo matching on high-res imagery. Many state-of-the-art (SOTA) methods struggle to process high-res imagery because of memory constraints or…

cs.CV20192 cited

Inferring Distributions Over Depth from a Single Image

Gengshan Yang, Peiyun Hu, Deva Ramanan

When building a geometric scene understanding system for autonomous vehicles, it is crucial to know when the system might fail. Most contemporary approaches cast the problem as dep…