Gengshan Yang | 杨庚山
I'm a MS-PhD (2017-) student at CMU Robotics, advised by Prof. Deva Ramanan. My research is focused on computer vision and machine learning. I’m particularly interested in designing depth and motion perception algorithms that are efficient and generalizable.
I did undergrad at Xi'an Jiaotong University. I was also fortunate to intern at Google VisCAM, Argo AI, TuSimple, and SVCL at UC San Diego. Our proposal of 'Open world dyanmic 3D reconstruction for smart cities' won 2021 Qualcomm innovation fellowship.
Research
Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Huiwen Chang, Deva Ramanan, William T. Freeman, Ce Liu
CVPR, 2021
A template-free approach for articulated shape reconstruction from a single video by combining differentiable rendering and data-driven correspondence and segmentation priors.
Gengshan Yang, Deva Ramanan
CVPR, 2021
We propose a neural architecture powered by geometric reasoning that decomposes two frames into a rigid background and multiple moving rigid bodies, parameterized by 3D rigid transformations and depth.
Gengshan Yang, Deva Ramanan
CVPR, 2020 (Oral)
We describe a neural architecture to upgrade 2D optical flow to 3D scene flow using optical expansion, which reveals changes in depth of scene elements over frames, e.g., things moving closer will get bigger.
Gengshan Yang, Deva Ramanan
NeurIPS, 2019
We introduce several simple modifications to the optical flow volumetric layers that: 1) significantly reduces computation and parameters, 2) enables test-time adaptation of cost volume size, and 3) converges much faster.
Gengshan Yang, Joshua Manela, Michael Happold, Deva Ramanan
CVPR, 2019
To adress the problem of real-time stereo matching on high-res imagery, an end-to-end framework that searches for correspondences incrementally over a coarse-to-fine hierarchy is proposed.
Gengshan Yang, Peiyun Hu, Deva Ramanan
IROS, 2019
We recast the continuous problem of depth regression as discrete binary classification, whose output is the occupancy probabilities on a 3D voxel grid. Such output reliably and efficiently captures multi-modal depth distributions in ambiguous cases.