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About this paper
Ryosuke Hirai, Kohei Yamashita, Antoine Guédon, Ryo Kawahara, Vincent Lepetit, Ko Nishino
A method to reconstruct high-fidelity 3D geometry and appearance of rigidly moving objects from sparse, static multi-view videos is presented. It leverages opportunistic object motion to create virtual viewpoints, achieving superior surface normal accuracy and novel view synthesis by jointly optimizing object pose and a canonical 3D representation with a motion-aware appearance model.
Key insights
- Opportunistic object motion can effectively overcome the inherent limitations of sparse static camera observations, providing sufficient information for detailed 3D reconstruction.
- A robust alternating optimization strategy is crucial for disentangling the tightly coupled problems of object pose estimation and canonical geometry refinement under sparse supervision.
- A physically-grounded, motion-aware appearance model is essential for accurately recovering fine geometric details and achieving photorealistic novel view synthesis for moving objects with specular surfaces.
Summary by alphaXiv.
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