H-PCC: Point Cloud Compression With Hybrid Mode Selection and Content Adaptive Down-Sampling

LiDAR sensors are integral to autonomous driving and augmented reality applications, providing essential depth information. However, managing the substantial volume of LiDAR point cloud data is crucial for practical application, necessitating efficient compression algorithms. Similar to other data compression domains, point cloud compression aims to spatially and temporally decorrelate information while preserving reconstruction quality. Addressing real-world challenges such as sensor pose retrieval inaccuracies or motion estimation failures, we introduce H-PCC, a hybrid LiDAR data compression pipeline that combines a range image prediction-based dynamic compression method with a novel point clustering octree-based static method. Additionally, we demonstrate that our content adaptive down-sampling technique using associated camera images can significantly enhance the LiDAR data compression rate with minimal impact on the accuracy of machine perception tasks. Extensive evaluations on the KITTI and Oxford datasets, using both traditional perceptual quality metrics and machine vision tasks, show that H-PCC surpasses state-of-the-art LiDAR data compression methods.

I. Introduction

Light detection and ranging (LiDAR) sensor measures the travel time of laser beams to calculate the distances from the sensor to targets. It has been widely used in autonomous driving and augmented/virtual reality applications to provide a rich 3D understanding of the scene. However, as the resolution of LiDAR sensors keeps rising, the amount of LiDAR data scales up to millions of points per ‘frame’, which poses a significant challenge for both memory and computation bandwidth. Thus, it is of great importance to develop an efficient LiDAR data compression algorithm to facilitate real-world 3D applications.