FLiCR: A Fast and Lightweight LiDAR Point Cloud Compression Based on Lossy RI
FLiCR: A Fast and Lightweight LiDAR Point Cloud Compression Based on Lossy RI
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DOI：
10.1109/sec54971.2022.00012
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发表时间：
2022-12
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影响因子：
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通讯作者：
Jin Heo;Christopher Phillips;Ada Gavrilovska
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作者：
Jin Heo;Christopher Phillips;Ada Gavrilovska
光探测和测距 (LiDAR) 传感器正在现代移动设备上可用，并提供 3D 传感功能。这种新功能有利于各种用例中的感知，但由于计算复杂度较高，资源受限的移动设备实时使用感知具有挑战性。在这种情况下，边缘计算可用于实现LiDAR在线感知，但由于LiDAR点云数据量大，将感知卸载到边缘服务器上需要低延迟、轻量级和高效的压缩。本文提出了 FLiCR，一种快速、轻量级的 LiDAR 点云压缩方法，用于实现边缘辅助在线感知。 FLiCR 基于范围图像 (RI) 作为中间表示 (IR)，以及用于压缩 RI 的字典编码。 FLiCR 通过利用有损 RI 来实现其优势，并且我们表明，通过量化和子采样，字节流压缩的效率大大提高。此外，我们还确定了当前质量指标在表示点云熵方面的局限性，并引入了一种新指标，可以反映有损 IR 的逐点质量和逐熵质量。评估结果表明，FLiCR 比现有的 LiDAR 压缩更适合边缘辅助实时感知，并且我们通过对 3D 目标检测和 LiDAR SLAM 的评估证明了压缩和度量的有效性。
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.