MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
This work addresses storage efficiency for LiDAR data in applications like autonomous driving, representing an incremental improvement over existing compression techniques.
The paper tackles the problem of compressing LiDAR sensor data streams by exploiting spatio-temporal relationships across multiple sweeps, resulting in a reduction of 7-17% and 15-35% in joint geometry and intensity bitrate on UrbanCity and SemanticKITTI datasets compared to prior state-of-the-art methods.
We present a novel compression algorithm for reducing the storage of LiDAR sensor data streams. Our model exploits spatio-temporal relationships across multiple LiDAR sweeps to reduce the bitrate of both geometry and intensity values. Towards this goal, we propose a novel conditional entropy model that models the probabilities of the octree symbols by considering both coarse level geometry and previous sweeps' geometric and intensity information. We then use the learned probability to encode the full data stream into a compact one. Our experiments demonstrate that our method significantly reduces the joint geometry and intensity bitrate over prior state-of-the-art LiDAR compression methods, with a reduction of 7-17% and 15-35% on the UrbanCity and SemanticKITTI datasets respectively.