History Dependent Significance Coding For incremental Neural Network Compression

Most existing correspondence-free registration methods suffer from performance degradation in partial overlapped point clouds. To solve the partial overlapped point cloud registration, this paper proposes, \textbf{SegReg}, a soft \underline{Seg}mentation-based correspondence-free \underline{Reg}istration approach. Specifically, we first softly segment both source and target point clouds into a discrete number of geometric partitions, respectively. Then registration is achieved through iteratively using the IC-LK algorithm to minimize the distance between the feature descriptors of the corresponded partitions. Extensive experiments on synthetic synthetic dataset ModelNet40 and real dataset 7Scene show that the proposed method achieves state-of-the-art performance.