Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover

• URL: http://arxiv.org/abs/2408.14997v2
• Date: Mon, 16 Sep 2024 07:05:56 GMT
• Title: Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover
• Authors: Ran Yu, Haixin Yu, Shoujie Li, Huang Yan, Ziwu Song, Wenbo Ding,
• Abstract summary: 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. We further develop a real-world human-to-robot handover system based on HADR, demonstrating its potential in human-robot interaction applications.
• Score: 5.329513275750882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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