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arXiv · 2405.05858

Free-Moving Object Reconstruction and Pose Estimation with Virtual Camera

Haixin Shi·Yinlin Hu·Daniel Koguciuk·Juan-Ting Lin·Mathieu Salzmann·David Ferstl
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Abstract

We propose an approach for reconstructing free-moving object from a monocular RGB video. Most existing methods either assume scene prior, hand pose prior, object category pose prior, or rely on local optimization with multiple sequence segments. We propose a method that allows free interaction with the object in front of a moving camera without relying on any prior, and optimizes the sequence globally without any segments. We progressively optimize the object shape and pose simultaneously based on an implicit neural representation. A key aspect of our method is a virtual camera system that reduces the search space of the optimization significantly. We evaluate our method on the standard HO3D dataset and a collection of egocentric RGB sequences captured with a head-mounted device. We demonstrate that our approach outperforms most methods significantly, and is on par with recent techniques that assume prior information.

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Haixin Shi, Yinlin Hu, Daniel Koguciuk, Juan-Ting Lin, Mathieu Salzmann, David Ferstl. 2024-05-09. Free-Moving Object Reconstruction and Pose Estimation with Virtual Camera. https://arxiv.org/abs/2405.05858

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