Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression

LiDAR point clouds provide explicit, deterministic physical boundaries critical for collaborative safety-critical perception. However, modern wireless channels are subject to dynamic bandwidth fluctuations and transient link outages. While most existing LiDAR codecs assume ideal channels and collapse under transmission disruptions, robust alternatives (such as deep JSCC or MDC) rely on statistical estimation, turning exact physical measurements into unverified algorithmic estimates. To address this dilemma, we propose Proteus, a learned LiDAR codec operating on 2D range images. Built upon a progressive bit-plane slicing representation, Proteus inherently ensures that stream truncation mathematically maps to deterministic spatial precision degradation without geometric hallucinations. To reconcile high-efficiency bits-back coding with stream-level truncation robustness, Proteus decouples the frame representation into two independent coding pathways: (i) the significant range bit-planes (SIG), which employs a self-contained bits-back coder equipped with an Autoregressive Initial Bits (ArIB) mechanism to eliminate single-frame overhead while securing an essential perceptual lower bound; and (ii) the insignificant range bit-planes and attributes (INS), which serializes the remaining low range planes before subordinate attributes via FIFO range coding, gracefully shedding attribute precision first during bandwidth drops. Extensive experiments on the Waymo Open Dataset and SemanticKITTI demonstrate that Proteus tolerates up to approximately 70% bitstream truncation, while outperforming established standards (e.g., G-PCC) and the representative learned compressor Unicorn under ideal channel conditions.