SemICP: Semantic Non-Rigid Point Cloud Registration with Elastic Energy Regularization

2026-07-23 · Source: cs.CV updates on arXiv.org · Field: Science & Research — Health & Medical Research, Engineering & Applied Sciences, Mathematics & Computational Sciences · Depth: Expert, long

Summary

SemICP, a novel semantic Iterative Closest Point (ICP) method, addresses limitations in classical point cloud registration for computer-aided interventions (CAI) by incorporating semantic labels and linear elastic energy regularization. This approach improves robustness in closest point matching and introduces a new point cloud deformation representation for explicit biomechanical energy constraints. Experiments on the Learn2reg abdominal MR-CT registration dataset (8 intra-patient MR-CT pairs) and a trans-oral robotic surgery ultrasound-CT registration dataset (18 US-CT pairs from 7 patients) demonstrate that SemICP significantly reduces Hausdorff distance compared to other leading ICP-based methods. The method also features a rigid initialization that achieves better convergence across varying initializations and visible ratios, as shown in a sensitivity study simulating 10-80% visible points. Implemented with a 12GB Nvidia GeForce RTX 3060 GPU, Python 3.9.18, PyTorch 2.1.2, and CUDA-11.8, SemICP uses $20\times 20\times 20$ control points and assumes $E=1$kPa and $\nu=0.499$ for elastic energy.

Key takeaway

For AI Scientists and Robotics Engineers developing computer-aided intervention systems, SemICP offers a robust solution for non-rigid point cloud registration. If your applications require high accuracy in multi-organ registration with limited training data, you should consider integrating semantic labels and biomechanical energy constraints. This approach significantly improves registration accuracy, particularly for deformable structures, and enhances convergence stability, making it suitable for real-time surgical guidance where explainability is critical.

Key insights

SemICP enhances non-rigid point cloud registration by integrating semantic labels and explicit biomechanical elastic energy regularization for improved accuracy.

Principles

• Semantic labels improve point matching robustness.
• Biomechanical energy regularization yields realistic deformation.
• Control points enable consistent memory usage.

Method

SemICP performs rigid initialization using label-informed point-to-plane loss, followed by non-rigid refinement. It uses trilinear interpolation from control points and minimizes a loss function including linear elastic, magnitude, and gradient regularization.

In practice

• Apply semantic labels to improve point matching in medical imaging.
• Use control point-based deformation for biomechanical modeling.
• Implement linear elastic energy regularization for realistic tissue deformation.

Topics

• Point Cloud Registration
• Iterative Closest Point
• Semantic Segmentation
• Biomechanical Modeling
• Computer-Aided Intervention
• Medical Imaging

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.