Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover
<|reference_start|>Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover: Transparent objects are common in daily life, while their optical properties pose challenges for RGB-D cameras to capture accurate depth information. This issue is further amplified when these objects are hand-held, as hand occlusions further complicate depth estimation. For assistant robots, however, accurately perceiving hand-held transparent objects is critical to effective human-robot interaction. This paper presents a Hand-Aware Depth Restoration (HADR) method based on creating an implicit neural representation function from a single RGB-D image. The proposed method utilizes hand posture as an important guidance to leverage semantic and geometric information of hand-object interaction. To train and evaluate the proposed method, we create a high-fidelity synthetic dataset named TransHand-14K with a real-to-sim data generation scheme. Experiments show that our method has better performance and generalization ability compared with existing methods. We further develop a real-world human-to-robot handover system based on HADR, demonstrating its potential in human-robot interaction applications.<|reference_end|>
@article{yu2024depth,
    title={Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot
  Handover},
    author={Ran Yu, Haixin Yu, Shoujie Li, Huang Yan, Ziwu Song, Wenbo Ding},
    journal={arXiv preprint arXiv:2408.14997},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14997},
    primaryClass={cs.RO cs.CV}
}