I am a Ph.D. Intern at AVG working with Prof. Andreas Geiger and a Ph.D. student in Robotics at Georgia Tech, jointly advised by Prof. James Rehg, and Prof. Frank Dellaert. Before I started my Ph.D. at Gatech, I finished my Master thesis under the supervision of Prof. Andrew Davison at Imperial College London.
My research interests cover computer vision research particularly in the 3D scene understanding that can bridge perception to planning and control. My current focus is to explore fast and effective approaches for dense 3D motion (scene flow) from videos, which is the core of my thesis topic, which include but not limited to:
3D Scene Flow, Optical Flow and Stereo.
Semantic Scene Understanding
Structure from Motion, Simultaneous Localization and Mapping
In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, June 2019 (inproceedings)
In this paper, we provide a modern synthesis of the classic inverse compositional algorithm for dense image alignment. We first discuss the assumptions made by this well-established technique, and subsequently propose to relax these assumptions by incorporating data-driven priors into this model. More specifically, we unroll a robust version of the inverse compositional algorithm and replace multiple components of this algorithm using more expressive models whose parameters we train in an end-to-end fashion from data. Our experiments on several challenging 3D rigid motion estimation tasks demonstrate the advantages of combining optimization with learning-based techniques, outperforming the classic inverse compositional algorithm as well as data-driven image-to-pose regression approaches.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems