A Novel Coding Architecture for Multi-Line LiDAR Point Clouds Based on Clustering and Convolutional LSTM Network

Light detection and ranging (LiDAR) plays an indispensable role in autonomous driving technologies, such as localization, map building, navigation and object avoidance. However, due to the vast amount of data, transmission and storage could become an important bottleneck. In this article, we propose a novel compression architecture for multi-line LiDAR point cloud sequences based on clustering and convolutional long short-term memory (LSTM) networks. LiDAR point clouds are structured, which provides an opportunity to convert the 3D data to 2D array, represented as range images. Thus, we cast the 3D point clouds compression as a range image sequence compression problem. Inspired by the high efficiency video coding (HEVC) algorithm, we design a novel compression framework for LiDAR data that includes two main techniques: intra-prediction and inter-prediction. For intra-frames, inspired by the depth modeling modes (DMM) adopted in 3D-HEVC, we develop a clustering-based intra-prediction technique, which can utilize the spatial structure characteristics of point clouds to remove the spatial redundancy. For inter-frames, we design a prediction network model using convolutional LSTM cells. The network model is capable of predicting future inter-frames using the encoded intra-frames. As a result, temporal redundancy can be removed. Experiments on the KITTI dataset demonstrate that the proposed method achieves an impressive compression ratio (CR), with 4.10% at millimeter precision, which means the point clouds can compress to nearly 1/25 of their original size. Additionally, compared with the well-known octree, Google Draco, and MPEG TMC13 methods, our algorithm yields better performance in compression ratio.