Towards Dynamic 3D Reconstruction of Hand-Instrument Interaction in Ophthalmic Surgery

• URL: http://arxiv.org/abs/2505.17677v2
• Date: Fri, 30 May 2025 04:41:21 GMT
• Title: Towards Dynamic 3D Reconstruction of Hand-Instrument Interaction in Ophthalmic Surgery
• Authors: Ming Hu, Zhengdi Yu, Feilong Tang, Kaiwen Chen, Yulong Li, Imran Razzak, Junjun He, Tolga Birdal, Kaijing Zhou, Zongyuan Ge,
• Abstract summary: This work introduces OphNet-3D, the first extensive RGB-D dynamic 3D reconstruction dataset for ophthalmic surgery.<n>It comprises 41 sequences from 40 surgeons and totaling 7.1 million frames, with fine-grained annotations of 12 surgical phases, 10 instrument categories, dense MANO hand meshes, and full 6-DoF instrument poses.<n>Building upon OphNet-3D, we establish two challenging benchmarks-bimanual hand pose estimation and hand-instrument interaction reconstruction.
• Score: 38.9015512099686
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate 3D reconstruction of hands and instruments is critical for vision-based analysis of ophthalmic microsurgery, yet progress has been hampered by the lack of realistic, large-scale datasets and reliable annotation tools. In this work, we introduce OphNet-3D, the first extensive RGB-D dynamic 3D reconstruction dataset for ophthalmic surgery, comprising 41 sequences from 40 surgeons and totaling 7.1 million frames, with fine-grained annotations of 12 surgical phases, 10 instrument categories, dense MANO hand meshes, and full 6-DoF instrument poses. To scalably produce high-fidelity labels, we design a multi-stage automatic annotation pipeline that integrates multi-view data observation, data-driven motion prior with cross-view geometric consistency and biomechanical constraints, along with a combination of collision-aware interaction constraints for instrument interactions. Building upon OphNet-3D, we establish two challenging benchmarks-bimanual hand pose estimation and hand-instrument interaction reconstruction-and propose two dedicated architectures: H-Net for dual-hand mesh recovery and OH-Net for joint reconstruction of two-hand-two-instrument interactions. These models leverage a novel spatial reasoning module with weak-perspective camera modeling and collision-aware center-based representation. Both architectures outperform existing methods by substantial margins, achieving improvements of over 2mm in Mean Per Joint Position Error (MPJPE) and up to 23% in ADD-S metrics for hand and instrument reconstruction, respectively.

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