  Compressing a set of unordered points is far more challenging than
compressing images/videos of regular sample grids, because of the difficulties
in characterizing neighboring relations in an irregular layout of points. Many
researchers resort to voxelization to introduce regularity, but this approach
suffers from quantization loss. In this research, we use the KNN method to
determine the neighborhoods of raw surface points. This gives us a means to
determine the spatial context in which the latent features of 3D points are
compressed by arithmetic coding. As such, the conditional probability model is
adaptive to local geometry, leading to significant rate reduction.
Additionally, we propose a dual-layer architecture where a non-learning base
layer reconstructs the main structures of the point cloud at low complexity,
while a learned refinement layer focuses on preserving fine details. This
design leads to reductions in model complexity and coding latency by two orders
of magnitude compared to SOTA methods. Moreover, we incorporate an implicit
neural representation (INR) into the refinement layer, allowing the decoder to
sample points on the underlying surface at arbitrary densities. This work is
the first to effectively exploit content-aware local contexts for compressing
irregular raw point clouds, achieving high rate-distortion performance, low
complexity, and the ability to function as an arbitrary-scale upsampling
network simultaneously.
