  Lidars are depth measuring sensors widely used in autonomous driving and
augmented reality. However, the large volume of data produced by lidars can
lead to high costs in data storage and transmission. While lidar data can be
represented as two interchangeable representations: 3D point clouds and range
images, most previous work focus on compressing the generic 3D point clouds. In
this work, we show that directly compressing the range images can leverage the
lidar scanning pattern, compared to compressing the unprojected point clouds.
We propose a novel data-driven range image compression algorithm, named RIDDLE
(Range Image Deep DeLta Encoding). At its core is a deep model that predicts
the next pixel value in a raster scanning order, based on contextual laser
shots from both the current and past scans (represented as a 4D point cloud of
spherical coordinates and time). The deltas between predictions and original
values can then be compressed by entropy encoding. Evaluated on the Waymo Open
Dataset and KITTI, our method demonstrates significant improvement in the
compression rate (under the same distortion) compared to widely used point
cloud and range image compression algorithms as well as recent deep methods.
