I'm a MS-PhD (2017-) student at CMU Robotics, advised by Prof. Deva Ramanan. My research is focused on computer vision. I’m particularly interested in 3D reconstruction of dynamic structures and events.
I did undergrad at Xi'an Jiaotong University. I was also fortunate to intern at Meta AI, 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.
Given multiple casual videos capturing a deformable object, BANMo reconstructs an animatable 3D model in a differentiable volume rendering framework.
Given a long video or multiple short videos, ViSER jointly optimizes articulated 3D shapes and a pixel-surface embedding to establish dense correspondences over video frames.
Given several (8-16) unposed images of the same instance, NeRS optimizes for a textured 3D reconstruction along with the illumination parameters at test-time.
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.