1. Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover
- Author
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Yu, Ran, Yu, Haixin, Li, Shoujie, Yan, Huang, Song, Ziwu, and Ding, Wenbo
- Subjects
Computer Science - Robotics ,Computer Science - Computer Vision and Pattern Recognition - Abstract
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., Comment: 7 pages, 7 figures, conference
- Published
- 2024