Free-Moving Object Reconstruction and Pose Estimation with Virtual Camera
<|reference_start|>Free-Moving Object Reconstruction and Pose Estimation with Virtual Camera: 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.<|reference_end|>
@article{shi2024free-moving,
    title={Free-Moving Object Reconstruction and Pose Estimation with Virtual
  Camera},
    author={Haixin Shi, Yinlin Hu, Daniel Koguciuk, Juan-Ting Lin, Mathieu
  Salzmann, David Ferstl},
    journal={arXiv preprint arXiv:2405.05858},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05858},
    primaryClass={cs.CV cs.AI cs.GR cs.RO}
}