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

2026-08-01

AI Summary

This work addresses the degradation of LiDAR point cloud geometric integrity caused by bitstream truncation in wireless channels, a challenge inadequately tackled by existing robust compression methods that rely on unreliable algorithmic estimates. To this end, the paper proposes Proteus, a learnable codec based on 2D range images that decouples significant bitplanes (SIG) from insignificant bitplanes and attributes (INS). By integrating bitplane slicing representation, hybrid lossless-predictive reconstruction, and a priority-aware scheduling strategy, Proteus achieves, for the first time, stream-level truncation robustness—transforming arbitrary truncation into predictable spatial precision degradation while prioritizing geometric structure preservation. Experiments demonstrate that Proteus outperforms state-of-the-art methods, including G-PCC, Draco, JPEG XL, and Unicorn, on both Waymo and SemanticKITTI datasets, maintaining superior reconstruction quality even under approximately 70% bitstream truncation.

Abstract

LiDAR point clouds provide explicit, deterministic physical boundaries critical for collaborative safety-critical perception. However, wireless channels inherently impair and corrupt transmitted signals. Existing robust frameworks (such as deep JSCC or MDC) attempt to counter these channel impairments through statistical or parametric estimation, turning exact physical measurements into unverified algorithmic estimates. To address this, we propose Proteus, a learned LiDAR codec operating on 2D range images. By decoupling the frame representation into independent coders for the \textbf{sig}nificant range bit-planes (SIG) and the \textbf{ins}ignificant range bit-planes and attributes (INS), Proteus achieves overall stream-level truncation robustness. The non-truncatable SIG block encodes the most significant range bit-planes to establish a necessary, self-contained perceptual lower bound, below which the reconstructed point cloud is severely degraded. Meanwhile, INS employs bit-plane slicing representation and coding, ensuring that range truncation mathematically maps to a deterministic spatial precision degradation. Subordinate attributes are reconstructed via a hybrid lossless-predictive method, leveraging the decoded geometry as a strong structural prior for fine-grained approximation. Furthermore, strategic ordering within INS prioritizes geometry over attributes under bandwidth drops. Experimental results on the Waymo Open Dataset and SemanticKITTI demonstrate that Proteus tolerates up to approximately 70\% bitstream truncation, while outperforming established standards (G-PCC, Draco, and JPEG XL) and the representative learned compressor Unicorn under ideal channel conditions.

Problem

• LiDAR compression
• truncation robustness
• range image
• bitstream truncation
• safety-critical perception

Innovation

• truncation-robust
• LiDAR compression
• bit-plane coding
• range image representation
• learned codec