Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization
Researchers have developed Generalized-CVO, a novel method for local point cloud registration that eliminates the need for correspondence matching. This approach utilizes geometric surface structure and reproducing kernel Hilbert space embeddings to represent point clouds as continuous functions, enhancing alignment accuracy. The method incorporates a second-order on-manifold optimization scheme, which significantly speeds up computation compared to previous first-order solvers and demonstrates improved accuracy in diverse indoor and outdoor datasets. AI