Building rearticulable models for 3D articulated objects from multi-view RGB videos
Jiang, Wei
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https://hdl.handle.net/2142/125661
Description
Title
Building rearticulable models for 3D articulated objects from multi-view RGB videos
Author(s)
Jiang, Wei
Issue Date
2024-05-24
Director of Research (if dissertation) or Advisor (if thesis)
Wang, Shenlong
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
3D Articulated Object Understanding
Gaussian Splatting
Neural Rendering
Part Discovery
Abstract
This thesis presents a solution to the novel task of building a 3D rearticulable models from multi-view colored (RGB) videos, which enables to render the articulable object at sampled state, discover the rigid parts, and reconstruct the kinematic structure. The solution is comprised of Gaussian Articulated Implicit Model (GAIM), a representation for dynamic scenes with an articulable object, and a relax-and-project-based optimization framework. Evaluated on the Watch-It-Move (WIM) dataset, the proposed solution achieves on par view synthesis performance as other rendering baselines, and can effectively discover rigid parts and properly reconstruct the kinematic model of the observed articulated object.
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