ELiCv2: Learned LiDAR Geometry Compression under Real-Time Embedded Constraints

Abstract

Improving learned LiDAR compression increasingly relies on heavier inference, making it harder to meet the constraints of real-time embedded systems. We present ELiCv2, which removes two sources of redundant computation in progressive coding. Its discrete entropy interface selects a precomputed integer CDF instead of materializing one from a per-voxel distribution, while its occupancy-driven kernel-map builder derives finer-level sparse-convolution kernel-maps directly from the decoded hierarchy, eliminating coordinate-based neighbor search. ELiCv2 reinvests the recovered computation budget in lightweight ego-motion-aware temporal conditioning that gathers local occupancy context from the aligned previous reconstruction. On four LiDAR datasets and an NVIDIA Jetson Thor, ELiCv2 achieves a mean BD-rate reduction of 30.97% over G-PCC with channel width C=32 while sustaining the 20Hz nuScenes capture rate. Increasing the channel width to C=64 improves the mean reduction to 35.22%.