VISUAL DENSE THREE-DIMENSIONAL MOTION ESTIMATION IN THE WILD
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One of the most fundamental abilities of the human perception system is to seamlessly sense the changing 3D worlds from our ego-centric visual observations. Driven by the modern applications of robotics, autonomous driving, and mixed reality, machine perception requires a precise dense representation of 3D motion with low latency. In this thesis, we focus on the task of estimating absolute 3D motions in the world coordinate in unconstrained environments observed from ego-centric visual information only. The goal is to achieve a fast algorithm that can produce an accurate representation of the densely rich 3D motions. To achieve this goal, I propose to investigate the problem from four perspectives with the following contributions. 1) Present a fast and accurate continuous optimization approach that solves the scene motions as fixed-a-priori planar segments. 2) Present a learning-based approach that recovers the dense scene flow from egocentric motion and optical flow, decomposed by a novel data-driven rigidity prediction. 3) Present a modern synthesis of the classic inverse compositional method for 3D rigid motion estimation using dense image alignment. 4) Present a two-view monocular scene flow approach that recovers depth, camera motion, and 3D scene motions of rigid moving scenes.