3D Point Cloud Compression with Recurrent Neural Network and Image
  Compression Methods
Till Beemelmanns Yuchen Tao Bastian Lampe Lennart Reiher Raphael van Kempen Timo Woopen Lutz Eckstein
Storing and transmitting LiDAR point cloud data is essential for many AV
applications, such as training data collection, remote control, cloud services
or SLAM. However, due to the sparsity and unordered structure of the data, it
is difficult to compress point cloud data to a low volume. Transforming the raw
point cloud data into a dense 2D matrix structure is a promising way for
applying compression algorithms. We propose a new lossless and calibrated
3D-to-2D transformation which allows compression algorithms to efficiently
exploit spatial correlations within the 2D representation. To compress the
structured representation, we use common image compression methods and also a
self-supervised deep compression approach using a recurrent neural network. We
also rearrange the LiDAR's intensity measurements to a dense 2D representation
and propose a new metric to evaluate the compression performance of the
intensity. Compared to approaches that are based on generic octree point cloud
compression or based on raw point cloud data compression, our approach achieves
the best quantitative and visual performance. Source code and dataset are
available at https://github.com/ika-rwth-aachen/Point-Cloud-Compression.