Sinkhorn-CPD: Robust point cloud registration via unbalanced entropic optimal transport
Researchers have developed Sinkhorn-CPD, a novel method for point cloud registration that improves upon the traditional Coherent Point Drift (CPD) algorithm. By employing unbalanced entropic optimal transport, Sinkhorn-CPD can effectively handle outliers and partial overlaps, which are common challenges for CPD. The new approach utilizes dual Kullback-Leibler penalties and generalized Sinkhorn iterations for efficient computation. Experiments demonstrate that Sinkhorn-CPD achieves state-of-the-art accuracy and robust performance across various benchmarks. AI
IMPACT Enhances robustness in point cloud registration, potentially improving applications in robotics and 3D reconstruction.