This paper investigates the impact of standardized Geometry-based Point Cloud Compression (G-PCC) on LiDAR-based object detection tasks. It evaluates how compression distortion types, and distortion levels, together with point cloud density, and object semantics affect various 3D object detection methods, including point-based, voxel-based, and hybrid approaches, using the KITTI object detection dataset.

Due to the ability to depict large-scale 3D scenes, point clouds acquired by the Light Detection And Ranging (LiDAR) devices have played an indispensable role in various fields. The growing data amount of point cloud, however, brings huge challenges to existing point cloud processing networks. Developing point cloud compression algorithms has become an active research area in recent years. Representative compression frameworks include the MPEG Geometry-based Point Cloud Compression (G-PCC) standard in which a dedicated profile is designed for spinning LiDAR point clouds. In that design, prior knowledge of the LiDAR device is used to project points to nodes in a predictive structure which better reflects the spatial correlation of LiDAR point clouds. In this paper, an analysis has been conducted to explain the observed irregular point distribution in the predictive structure. A regularized projection algorithm is then proposed to construct a reliable prediction relationship in the predictive structure. Simplified geometry prediction techniques are further proposed based on the regularized projection pattern. Experimental results show that an average BD-rate gain of 18% can be achieved with lower encoding runtime if compared with MPEG G-PCC.

Point cloud is a major representation format of 3D objects and scenes. It has been increasingly applied in various applications due to the rapid advances in 3D sensing and rendering technologies. In the field of autonomous driving, point clouds captured by spinning Light Detection And Ranging (LiDAR) devices have become an informative data source for road environment perception and intelligent vehicle control. On the other hand, the massive data volume of point clouds also brings huge challenges to point cloud transmission and storage. Therefore, establishing compression frameworks and algorithms that conform to the characteristics of point cloud data has become an important research topic for both academia and industry. In this paper, a geometry compression method dedicated to spinning LiDAR point cloud was proposed taking advantage of the prior information of the LiDAR acquisition procedure. Rate-distortion optimizations were further integrated into the coding pipeline according to the characteristics of the prediction residuals. Experimental results obtained on different datasets show that the proposed method consistently outperforms the state-of-the-art G-PCC predictive geometry coding method with reduced runtime at both the encoder and decoder sides.

Point clouds captured by Light Detection And Ranging (Li-DAR) devices have played a significant role in autonomous driving and high-precision mapping. The massive amount of point cloud data, however, challenges the capacity of current data storage and transmission networks, which confines the development of LiDAR point cloud applications. To alleviate this situation, a novel grid-based geometry compression framework dedicated to spinning LiDAR point cloud is proposed in this paper. Firstly, a 2D grid-based point cloud representation is built taking advantage of the LiDAR acquisition pattern. Then, a projection is performed to effectively represent the 3D geometry as multiple 2D geometry components. Finally, dedicated prediction and entropy coding methods are designed for each 2D geometry component according to its characteristics. Experimental results show that the proposed method outperforms MPEG G-PCC with an average gain of 18.81% and 8.03% for lossy and lossless coding respectively.

Point clouds captured by spinning Light Detection And Ranging (LiDAR) devices have played a significant role in many applications. To efficiently store and transmit such a huge amount of data, MPEG designed the Geometry-based Point Cloud Compression (G-PCC) standard, which includes a dedicated Pre-dictive Profile for LiDAR point clouds. In this scheme, Cartesian coordinates are mapped to the spherical representation using the elevation-related LiDAR calibration parameters to better characterize the spherical acquisition pattern of the spinning LiDAR device. As such, stronger spatial correlations exist among neighbouring nodes in the predictive structure, resulting in higher compression efficiency. However, it should be mentioned that the azimuth-related calibration parameters, which are unused, also impact the accuracy and correctness of the mapped spherical coordinates. In this paper, an azimuth adjustment method is proposed taking into account this impact. Experimental results show that the proposed azimuth adjustment can consistently improve the coding efficiency of G-PCC. Furthermore, a LiDAR calibration parameter estimation method is proposed in case the azimuth-related parameters are absent. Results show that the proposed calibration parameter estimation can precisely approximate the ground truth.