研究激光雷达点云压缩对远程语义分割的影响，给出通信带宽需求
On the Impact of LiDAR Point Cloud Compression on Remote Semantic Segmentation
- 设计新失真度量评估压缩对分割性能影响
- G-PCC需0.6MB/s、L3C2需2.8MB/s保高质量分割
- 适合智能交通与自动驾驶系统资源规划者
自动驾驶依赖激光雷达生成三维点云以实现精准语义分割与目标检测。在智慧城市框架下，本文关注点云传输（压缩）对远程（云端）分割的影响，而非本地处理。我们提出一种新的适合性失真度量来评估压缩影响，并在语义KITTI数据集上测试了MPEG的两种压缩算法（G-PCC和L3C2）以及两种主流分割算法（2DPASS和PVKD）。结果表明，保持高分割质量需约0.6 MB/s通信吞吐量（G-PCC）和2.8 MB/s（L3C2），对自动驾驶导航基础设施规划具有重要意义。
原文摘要 · Abstract (English)
Autonomous vehicles rely on LiDAR sensors to generate 3D point clouds for accurate segmentation and object detection. In a context of a smart city framework, we would like to understand the effect that transmission (compression) can have on remote (cloud) segmentation, instead of local processing. In this short paper, we try to understand the impact of point cloud compression on semantic segmentation performance and to estimate the necessary bandwidth requirements. We developed a new (suitable) distortion metric to evaluate such an impact. Two of MPEG's compression algorithms (GPCC and L3C2) and two leading semantic segmentation algorithms (2DPASS and PVKD) were tested over the Semantic KITTI dataset. Results indicate that high segmentation quality requires communication throughput of approximately 0.6 MB/s for G-PCC and 2.8 MB/s for L3C2. These results are important in order to plan infrastructure resources for autonomous navigation.
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。