FLiCR：一种基于有损 RI 的快速轻量级 LiDAR 点云压缩方法
FLiCR: A Fast and Lightweight LiDAR Point Cloud Compression Based on Lossy RI
AI总结：
本文提出了一种基于有损距离图像的快速轻量级 LiDAR 点云压缩方法 FLiCR，通过量化和子采样提升压缩效率，并引入新指标评估有损中间表示质量，适用于边缘辅助的实时感知。
摘要：
激光雷达（LiDAR）传感器正逐渐在现代移动设备上普及，并提供 3D 感知能力。这种新能力有利于各种用例中的感知，但由于其计算复杂度高，资源受限的移动设备难以实时使用这些感知。在此背景下，边缘计算可用于实现 LiDAR 在线感知，但由于 LiDAR 点云数据量庞大，在边缘服务器上卸载感知需要低延迟、轻量且高效的压缩。本文提出了 FLiCR，一种用于实现边缘辅助在线感知的快速轻量级 LiDAR 点云压缩方法。FLiCR 以距离图像（RI）作为中间表示（IR），并采用字典编码来压缩 RI。FLiCR 通过利用有损 RI 实现其优势，我们展示了量化和子采样大幅提升了字节流压缩的效率。此外，我们指出了当前质量指标在表示点云熵方面的局限性，并引入了一个新指标，该指标同时反映有损 IR 的逐点质量和熵质量。评估结果表明，与现有的 LiDAR 压缩方法相比，FLiCR 更适合边缘辅助的实时感知，并且我们通过对 3D 物体检测和 LiDAR SLAM 的评估证明了压缩方法和指标的有效性。
英文摘要：
Light detection and ranging (LiDAR) sensors are becoming available on modern mobile devices and provide a 3D sensing capability. This new capability is beneficial for perceptions in various use cases, but it is challenging for resource-constrained mobile devices to use the perceptions in real-time because of their high computational complexity. In this context, edge computing can be used to enable LiDAR online perceptions, but offloading the perceptions on the edge server requires a low-latency, lightweight, and efficient compression due to the large volume of LiDAR point clouds data. This paper presents FLiCR, a fast and lightweight LiDAR point cloud compression method for enabling edge-assisted online perceptions. FLiCR is based on range images (RI) as an intermediate representation (IR), and dictionary coding for compressing RIs. FLiCR achieves its benefits by leveraging lossy RIs, and we show the efficiency of bytestream compression is largely improved with quantization and subsampling. In addition, we identify the limitation of current quality metrics for presenting the entropy of a point cloud, and introduce a new metric that reflects both point-wise and entropy-wise qualities for lossy IRs. The evaluation results show FLiCR is more suitable for edge-assisted real-time perceptions than the existing LiDAR compressions, and we demonstrate the effectiveness of our compression and metric with the evaluations on 3D object detection and LiDAR SLAM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
