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A Novel Coding Architecture for Lidar Point Cloud Sequence

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A Novel Coding Architecture for Lidar Point Cloud Sequence This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/LRA.2020.3010207, IEEE Robotics and Automation Letters IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED JUNE, 2020 1 A Novel Coding Architecture for LiDAR Point Cloud Sequence Xuebin Sun1*, Sukai Wang2*, Miaohui Wang3, Zheng Wang4 and Ming Liu2, Senior Member, IEEE Abstract—In this paper, we propose a novel coding architecture detection [2], etc. The enormous volume of LiDAR point cloud for LiDAR point cloud sequences based on clustering and data could be an important bottleneck for transmission and prediction neural networks. LiDAR point clouds are structured, storage. Therefore, it is highly desirable to develop an efficient which provides an opportunity to convert the 3D data to a 2D array, represented as range images. Thus, we cast the coding algorithm to satisfy the requirement of autonomous LiDAR point clouds compression as a range images coding driving. problem. Inspired by the high efficiency video coding (HEVC) Octree methods have been widely researched for point cloud algorithm, we design a novel coding architecture for the point compression. The main idea of octree-based coding methods cloud sequence. The scans are divided into two categories: intra- is to recursively subdivide the current data according to the frames and inter-frames. For intra-frames, a cluster-based intra- prediction technique is utilized to remove the spatial redundancy. range of coordinates from top to bottom, and gradually form an For inter-frames, we design a prediction network model using octree adaptive structure. Octree method can hardly compress convolutional LSTM cells, which is capable of predicting future LiDAR data into very small volumes with low information inter-frames according to the encoded intra-frames. Thus, the loss. The vehicle-mounted LiDAR data is structured, which temporal redundancy can be removed. Experiments on the KITTI provides a chance to convert them into a 2D panorama range data set show that the proposed method achieves an impressive compression ratio, with 4.10% at millimeter precision. Compared image. Some researchers focus on using image-based coding with octree, Google Draco and MPEG TMC13 methods, our methods to compress the point cloud data. However, these scheme also yields better performance in compression ratio. methods are unsuitable for unmanned vehicles. Traditional Index Terms—Range Sensing, Automation Technologies for image or video encoding algorithms, such as JPEG2000 , Smart Cities JPEG-LS [3], and HEVC [4], were designed mostly for encoding integer pixel values, and using them to encode I. INTRODUCTION floating-point LiDAR data will cause significant distortion. Furthermore, the range image is characterized by sharp edges A. Motivation and homogeneous regions with nearly constant values, which DVANCES in autonomous driving technology have is quite different from textured video. Thus, coding the range A widened the use of 3D data acquisition techniques. image with traditional tools such as the block-based discrete LiDAR is almost indispensable for outdoor mobile robots, and cosine transform (DCT) followed by coarse quantization can plays a fundamental role in many autonomous driving appli- result in significant coding errors at sharp edges, causing a cations such as localization , path planning [1], and obstacle safety hazard in autonomous driving. Manuscript received: February, 24, 2019; Revised May, 22, 2019; Accepted In this research, we address the LiDAR point cloud se- June, 24, 2020. quence compression problem. Learning from the HEVC ar- This paper was recommended for publication by Editor Behnke Sven chitecture, we propose a novel coding architecture for Li- upon evaluation of the Associate Editor and Reviewers' comments. This work was supported by National Natural Science Foundation of China No. DAR point cloud sequences, which mainly consists of intra- U1713211, and the Research Grant Council of Hong Kong SAR Government, prediction and inter-prediction technologies. For-intra-frames, China, under Project No. 11210017, awarded to Prof. Ming Liu. It was we utilize a cluster-based intra-prediction method to remove also supported in part by the National Natural Science Foundation of China (61701310, 61902251), in part by Natural Science Foundation of Guangdong the spatial redundancy. There are great structural similarities Province (2019A1515010961), and in part by Natural Science Foundation of between adjacent point clouds. For inter-frames, we train a Shenzhen City (JCYJ20180305124209486), awarded to Prof. Miaohui Wang. prediction neural network, which is capable of generating (Corresponding author: Ming Liu.) * The first two authors contributed equally to this work. the future inter-frames using the encoded intra-frames. The 1 Xuebin Sun is now with the Department of Mechanical and Au- intra- and inter-residual data is quantified and coded using tomation Engineering, The Chinese University of Hong Kong, Hong Kong. lossless coding schemes. Experiments on the KITTI dataset He contributed to this work during his time at HKUST. (email: sunxue- [email protected]; [email protected]) demonstrate our method yields an impressive performance. 2 Sukai Wang and Ming Liu are with the Department of Electronic & Computer Engineering, Hong Kong University of Science and Technology, Hong Kong. (email: [email protected]; [email protected]) B. Contributions 3 Miaohui Wang is with the College of Electrical and Information Engi- neering, Shenzhen University, China. (email: [email protected]) In our previous paper [5], an efficient compression algo- 4 Zheng Wang is with the Department of Mechanical Engineering, the rithm for a single scan is developed based on clustering. University of Hong Kong, Hong Kong, also with the Department of Mechan- Based on this previous technique, we propose a novel coding ical and Energy Engineering, Southern University of Science and Technology, China. (email: [email protected]) architecture for LiDAR point cloud sequences in this work. Digital Object Identifier (DOI): see top of this page. The contributions of the paper are summarized as follows. 2377-3766 (c) 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. Authorized licensed use limited to: Hong Kong University of Science and Technology. Downloaded on July 19,2020 at 04:45:39 UTC from IEEE Xplore. Restrictions apply. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/LRA.2020.3010207, IEEE Robotics and Automation Letters 2 IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED JUNE, 2020 • Learning from the HEVC algorithm, we develop a novel cloud data. Tang et al. [12] present an octree-based scattered coding architecture for LiDAR point cloud sequences. point cloud compression algorithm. Their method improves • For inter-frames, we design a prediction network model the stop condition of segmentation to ensure appropriate voxel using convolutional LSTM cells. The network model is size. Additionally, using their method, the spatial redundancy capable of predicting future inter-frames according to the and outliers can be removed by traversal queries and bit encoded intra-frames. manipulation. • The coding scheme is specially designed for LiDAR point Structured point cloud sequence compression: Kammerl cloud sequences for autonomous driving. Compared with et al. [10] introduce a novel lossy compression method for octree, Google Draco and MPEG TMC13 methods, our point cloud streams to remove the spatial and temporal re- method yields better performance. dundancy within the point data. Octree data structures are used to code the intra point cloud data. Additionally, they C. Organization develop a technique for contrasting the octree data structures of consecutive point clouds. By encoding structural differences, The rest of this paper is organized as follows. In Section spatial redundancy can be removed. Tu et al. [13] propose II, the related works are reviewed. Section III gives an an image-based compression method for LiDAR point cloud overview of the point cloud sequence compression scheme. sequences. They convert the LiDAR data losslessly into range The inter-prediciton techniques is presented in Section IV. The images, and then use the standard image or video coding experimental results are presented and discussed in Section V. techniques to reduce the volume of the data. As image-based Finally, the conclusion and discussion are given in Section VI. compression methods hardly utilize the 3D characteristics of point clouds, Tu et al. propose a SLAM-based prediction II. RELATED WORK for continuous point cloud data in their follow-up work The compression of 3D point cloud data has been widely [14]. Experimental results show that the SLAM-based method researched in literature. According to the types of point cloud outperforms image-based point cloud compression methods. data, compression algorithms can be roughly classified into In [15], Tu et al. develop a recurrent neural network with four categories. residual blocks for LiDAR point cloud streams compression. Structured single point cloud compression: Some re- Their
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