Adaptive point cloud compression based on precision-aware floating-point encoding

In distributed autonomous driving simulation systems, the autonomous driving algorithm and the simulator are usually deployed on different nodes. The simulator sends real-time sensor data, including 3D point clouds, to the algorithm. 3D point clouds captured by LiDAR (Light Detection and Ranging) are large and require high transmission performance. Insufficient bandwidth can significantly increase latency in point cloud transmission. This paper proposes a precision-aware floating-point encoding method to reduce the data size of the point cloud with an acceptable level of error while maintaining brilliant performance. Point cloud precision and spatial distribution exhibit direct dependencies on LiDAR configurations, while network transmission demonstrates dynamic bandwidth variations. This paper proposes a precision-adaptive floating-point compression framework that enables real-time adaptation of point cloud representations through coordinated analysis of LiDAR parameters and network conditions. Experimental evaluation demonstrates substantial latency reduction (up to 56.2%) under constrained bandwidth scenarios, and improved system resilience against network fluctuations through dynamic bitrate adaptation.