  Point cloud compression (PCC) is a key enabler for various 3-D applications,
owing to the universality of the point cloud format. Ideally, 3D point clouds
endeavor to depict object/scene surfaces that are continuous. Practically, as a
set of discrete samples, point clouds are locally disconnected and sparsely
distributed. This sparse nature is hindering the discovery of local correlation
among points for compression. Motivated by an analysis with fractal dimension,
we propose a heterogeneous approach with deep learning for lossy point cloud
geometry compression. On top of a base layer compressing a coarse
representation of the input, an enhancement layer is designed to cope with the
challenging geometric residual/details. Specifically, a point-based network is
applied to convert the erratic local details to latent features residing on the
coarse point cloud. Then a sparse convolutional neural network operating on the
coarse point cloud is launched. It utilizes the continuity/smoothness of the
coarse geometry to compress the latent features as an enhancement bit-stream
that greatly benefits the reconstruction quality. When this bit-stream is
unavailable, e.g., due to packet loss, we support a skip mode with the same
architecture which generates geometric details from the coarse point cloud
directly. Experimentation on both dense and sparse point clouds demonstrate the
state-of-the-art compression performance achieved by our proposal. Our code is
available at https://github.com/InterDigitalInc/GRASP-Net.
