  We propose a new paradigm for encoding the geometry of point cloud sequences,
where the convolutional neural network (CNN) which estimates the encoding
distributions is optimized on several frames of the sequence to be compressed.
We adopt lightweight CNN structures, we perform training as part of the
encoding process, and the CNN parameters are transmitted as part of the
bitstream. The newly proposed encoding scheme operates on the octree
representation for each point cloud, encoding consecutively each octree
resolution layer. At every octree resolution layer, the voxel grid is traversed
section-by-section (each section being perpendicular to a selected coordinate
axis) and in each section the occupancies of groups of two-by-two voxels are
encoded at once, in a single arithmetic coding operation. A context for the
conditional encoding distribution is defined for each two-by-two group of
voxels, based on the information available about the occupancy of neighbor
voxels in the current and lower resolution layers of the octree. The CNN
estimates the probability distributions of occupancy patterns of all voxel
groups from one section in four phases. In each new phase the contexts are
updated with the occupancies encoded in the previous phase, and each phase
estimates the probabilities in parallel, providing a reasonable trade-off
between the parallelism of processing and the informativeness of the contexts.
The CNN training time is comparable to the time spent in the remaining encoding
steps, leading to competitive overall encoding times. Bitrates and
encoding-decoding times compare favorably with those of recently published
compression schemes.
