ELiC通过跨比特深度特征传播实现高效激光雷达几何压缩
ELiC: Efficient LiDAR Geometry Compression via Cross-Bit-depth Feature Propagation and Bag-of-Encoders
- 跨比特深度传递特征，复用低密度层信息提升高密度层预测
- 在Ford和SemanticKITTI上达到实时压缩性能，压缩率领先
- 适合需要低延迟激光雷达数据压缩的自动驾驶系统
分层激光雷达几何压缩从低到高比特深度编码体素占据状态，但以往方法在各层级独立处理，需重新估计局部上下文，限制压缩效率。我们提出ELiC，一个实时框架，结合跨比特深度特征传播、袋式编码器（BoE）选择机制与保持Morton序的层次结构。跨比特深度传播利用更密集的低比特层特征支持稀疏的高比特层预测。BoE为每层从小型网络池中选择最适配的编码器，根据占据统计自适应容量，无需为每层单独训练模型。Morton层次结构在深度转换时保持全局Z序，消除逐层排序，降低延迟。这些组件协同提升熵建模与计算效率，在Ford和SemanticKITTI数据集上实现顶尖压缩性能与实时吞吐。代码与预训练模型已公开于https://github.com/moolgom/ELiCv1。
原文摘要 · Abstract (English)
Hierarchical LiDAR geometry compression encodes voxel occupancies from low to high bit-depths, yet prior methods treat each depth independently and re-estimate local context from coordinates at every level, limiting compression efficiency. We present ELiC, a real-time framework that combines cross-bit-depth feature propagation, a Bag-of-Encoders (BoE) selection scheme, and a Morton-order-preserving hierarchy. Cross-bit-depth propagation reuses features extracted at denser, lower depths to support prediction at sparser, higher depths. BoE selects, per depth, the most suitable coding network from a small pool, adapting capacity to observed occupancy statistics without training a separate model for each level. The Morton hierarchy maintains global Z-order across depth transitions, eliminating per-level sorting and reducing latency. Together these components improve entropy modeling and computation efficiency, yielding state-of-the-art compression at real-time throughput on Ford and SemanticKITTI. Code and pretrained models are available at https://github.com/moolgom/ELiCv1.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。