论文标题
实时时空发光雷达点云压缩
Real-Time Spatio-Temporal LiDAR Point Cloud Compression
论文作者
论文摘要
实时压缩巨大的激光雷达点云对于无人机和自动驾驶汽车等自动机器至关重要。尽管最近的大多数先前工作都集中在压缩各个点云框架上，但本文提出了一个有效压缩点云序列的新型系统。用一系列点云框架来利用空间和时间冗余的想法。我们首先在点云序列中识别一个关键框架，并通过迭代平面拟合在空间上编码关键框架。然后，我们利用了一个事实，即连续的点云在物理空间中具有很大的重叠，因此可以（重新）用于编码时间流的空间编码数据。通过重复使用空间编码数据的时间编码不仅可以提高压缩率，还可以避免冗余计算，从而显着提高了压缩速度。实验表明，我们的压缩系统达到40倍至90倍的压缩率，显着高于MPEG的LiDAR点云压缩标准，同时保留了高端到端的应用精度。同时，我们的压缩系统具有压缩速度，该压缩速度与今天的激光射频相匹配，并且超过现有的压缩系统，从而实现了实时点云传输。
Compressing massive LiDAR point clouds in real-time is critical to autonomous machines such as drones and self-driving cars. While most of the recent prior work has focused on compressing individual point cloud frames, this paper proposes a novel system that effectively compresses a sequence of point clouds. The idea to exploit both the spatial and temporal redundancies in a sequence of point cloud frames. We first identify a key frame in a point cloud sequence and spatially encode the key frame by iterative plane fitting. We then exploit the fact that consecutive point clouds have large overlaps in the physical space, and thus spatially encoded data can be (re-)used to encode the temporal stream. Temporal encoding by reusing spatial encoding data not only improves the compression rate, but also avoids redundant computations, which significantly improves the compression speed. Experiments show that our compression system achieves 40x to 90x compression rate, significantly higher than the MPEG's LiDAR point cloud compression standard, while retaining high end-to-end application accuracies. Meanwhile, our compression system has a compression speed that matches the point cloud generation rate by today LiDARs and out-performs existing compression systems, enabling real-time point cloud transmission.
