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.
A template-free approach for articulated shape reconstruction from a single video by combining differentiable rendering and data-driven correspondence and segmentation priors.
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.
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.
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.
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.