A Novel Coding Architecture for LiDAR Point Cloud Sequence

Abstract

In this letter, we propose a novel coding architecture for LiDAR point cloud sequences based on clustering and prediction neural networks. LiDAR point clouds are structured, which provides an opportunity to convert the 3D data to a 2D array, represented as range images. Thus, we cast the LiDAR point clouds compression as a range images coding problem. Inspired by the high efficiency video coding (HEVC) algorithm, we design a novel coding architecture for the point cloud sequence. The scans are divided into two categories: intra-frames and inter-frames. For intra-frames, a cluster-based intra-prediction technique is utilized to remove the spatial redundancy. For inter-frames, we design a prediction network model using convolutional LSTM cells, which is capable of predicting future inter-frames according to the encoded intra-frames. Thus, the temporal redundancy can be removed. Experiments on the KITTI data set show that the proposed method achieves an impressive compression ratio, with 4.10% at millimeter precision. Compared with octree, Google Draco and MPEG TMC13 methods, our scheme also yields better performance in compression ratio.