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. Note to Practitioners—This article deals with the problem of efficient compression of point cloud sequences that come from light detection and ranging (LiDARs) mounted on autonomous mobile robots. The vast amount of point cloud data could be an important bottleneck for transmission and storage. Inspired by the HEVC algorithm, we develop a novel coding architecture for the point cloud sequence. The scans are divided into intraframe and interframe, which are encoded separately using different Manuscript received October 23, 2020; revised April 10, 2021; accepted May 7, 2021. This article was recommended for publication by Associate Editor P. Tokekar and Editor D. O. Papa upon evaluation of the reviewers’ comments. (Corresponding authors: Shing Shin Cheng; Ming Liu.) Xuebin Sun is with the College of Electrical and Information Engineering, Shenzhen University, Shenzhen 518060, China (e-mail: sunxuebin@szu.edu.cn; sunxuebin@tju.edu.cn). Yuxiang Sun is with the Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong (e-mail: yx.sun@polyu.edu.hk; sun.yuxiang@outlook.com). Weixun Zuo is with Shenzhen Unity Drive Innovation Technology Company Ltd., Shenzhen 518000, China (e-mail: zuoweixun@gmail.com). Shing Shin Cheng is with the Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong (e-mail: sscheng@cuhk.edu.hk). Ming Liu is with the Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong (e-mail: eelium@ust.hk). Color versions of one or more figures in this article are available at https://doi.org/10.1109/TASE.2021.3082196. Digital Object Identifier 10.1109/TASE.2021.3082196 techniques. Our method can be used for the compression of LiDAR point cloud sequences or dense LiDAR point cloud map and will significantly reduce the transmission bandwidth and storage spaces. We have to admit that although our method is less effective for real-time solutions, it can be highly efficient for off-line applications. Future studies will concentrate on further optimizing the coding algorithm to reduce the computational complexity and trying to find a balance between them.