Vis2Reg: Visibility-Aware Landmark-Free Geometric 3D--2D Registration for Liver Laparoscopy

• URL: http://arxiv.org/abs/2607.17810v1
• Date: Mon, 20 Jul 2026 10:56:52 GMT
• Title: Vis2Reg: Visibility-Aware Landmark-Free Geometric 3D--2D Registration for Liver Laparoscopy
• Abstract summary: Accurate 3D--2D liver registration is critical for AR-guided laparoscopic surgery.<n>We propose Vis2Reg, a visibility-aware registration framework that constrains deformation using mask-consistent visible regions.<n>Vis2Reg achieves a Dice score of 92.6% and a Chamfer Distance of 1.43 mm on real datasets, with 111 ms per-frame inference time.
• Score: 3.3028711693359316
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate 3D--2D liver registration, which aligns preoperative 3D models to partial, view-dependent intraoperative surface observations, is critical for AR-guided laparoscopic surgery but remains challenging due to severe occlusion, limited visibility, and the lack of 3D ground-truth supervision. Existing landmark-free approaches perform partial-to-complete geometric alignment, yet robust self-supervision under extreme partial visibility remains difficult. We propose Vis2Reg, a visibility-aware registration framework that explicitly constrains deformation using mask-consistent visible regions. We introduce a visibility-aware self-supervision that derives a visible-domain 3D supervision signal from intraoperative masks, enabled by differentiable point rasterization and mask-guided back-projection. This formulation improves robustness under severe occlusion while maintaining fully self-supervised learning. Vis2Reg combines a robust geometric rigid initialization module with an implicit neural deformation field for stable alignment. Vis2Reg achieves a Dice score of 92.6\% and a Chamfer Distance of 1.43 mm on real intraoperative datasets, with 111 ms per-frame inference time, demonstrating both accuracy and practical efficiency.

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