  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.
