发表机构
Peking University; Sun Yat-sen University（北京大学; 中山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出名为Proteus的激光雷达压缩编解码器，通过解耦编码实现抗70%比特流截断，在理想信道下优于G-PCC、Draco等标准及Unicorn，可用于安全关键感知场景。
AI 中文摘要
激光雷达点云提供了明确、确定的物理边界，这对协作式安全关键感知至关重要。然而，无线信道会固有地损害和损坏传输的信号。现有的鲁棒框架（如深度联合源信道编码（deep JSCC）或多描述编码（MDC））试图通过统计或参数估计来对抗这些信道损害，将精确的物理测量转化为未经证实的算法估计。为解决该问题，我们提出了Proteus，一种基于二维距离图像的学习型激光雷达编解码器。通过将帧表示解耦为显著距离比特平面（SIG）和非显著距离比特平面与属性（INS）的独立编码器，Proteus实现了整体流级抗截断性。不可截断的SIG模块对最重要的距离比特平面进行编码，以建立必要的、自包含的感知下界，低于该下界的重建点云会严重退化。同时，INS采用比特平面切片表示与编码，确保距离截断在数学上对应于确定性的空间精度退化。从属属性通过混合无损预测方法进行重建，利用解码后的几何结构作为强结构先验进行细粒度近似。此外，INS内的策略性排序在带宽下降时优先考虑几何结构而非属性。在Waymo开放数据集和SemanticKITTI上的实验表明，Proteus可耐受约70%的比特流弃权（不执行），且在理想信道条件下，其性能优于既定标准（G-PCC、Draco和JPEG XL）以及代表性学习型压缩器Unicorn。
英文摘要
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.
