A Novel Coding Scheme for Large-Scale Point Cloud Sequences Based on Clustering and Registration

Due to the huge volume of point cloud data, storing and transmitting it is currently difficult and expensive in autonomous driving. Learning from the high-efficiency video coding (HEVC) framework, we propose a novel compression scheme for large-scale point cloud sequences, in which several techniques have been developed to remove the spatial and temporal redundancy. The proposed strategy consists mainly of three parts: intracoding, intercoding, and residual data coding. For intracoding, inspired by the depth modeling modes (DMMs), in 3-D HEVC (3-D-HEVC), a cluster-based prediction method is proposed to remove the spatial redundancy. For intercoding, a point cloud registration algorithm is utilized to transform two adjacent point clouds into the same coordinate system. By calculating the residual map of their corresponding depth image, the temporal redundancy can be removed. Finally, the residual data are compressed either by lossless or lossy methods. Our approach can deal with multiple types of point cloud data, from simple to more complex. The lossless method can compress the point cloud data to 3.63% of its original size by intracoding and 2.99% by intercoding without distance distortion. Experiments on the KITTI dataset also demonstrate that our method yields better performance compared with recent well-known methods.