Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization
arXiv:2606.10019v1 Announce Type: cross Abstract: We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilbert space (RKHS) embeddings. The method represents point clouds as continuous functions with point-wise anisotropic kernels that encode local geometry. This formulation improves alignment along surface normals while relaxing alignment along tangential directions. To solve the resulting registration problem, we propose a second-order on-manifold optimization scheme with approximate Riemannian Hessi
The paper leverages recent advancements in geometric surface structure analysis and RKHS embeddings, indicating an accelerating trend in AI for spatial understanding and robotic perception.
This development can significantly improve the efficiency and accuracy of robotic systems, autonomous vehicles, and AR/VR applications by enabling robust real-time environmental understanding.
Local point cloud registration will become faster and more robust, particularly in complex environments where correspondence-free methods are crucial for reliable operation.
- · Robotics industry
- · Autonomous vehicle companies
- · AR/VR developers
- · Industrial automation
More reliable and efficient operation of robots and autonomous systems in unstructured environments.
Accelerated development and adoption of humanoid robots and advanced manufacturing processes.
Reduced costs and increased capabilities for logistics, inspection, and manipulation tasks performed by intelligent machines.
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Read at arXiv cs.AI