Point Cloud Geometry Compression Via Neural Graph Sampling

Compressing point cloud geometry (PCG) efficiently is of great interests for enabling abundant networked applications, because PCG is a promising representation to precisely illustrate arbitrary-shaped 3D objects and relevant physical scenes. To well exploit the unconstrained geometric correlation of input PCG, a three-step neural graph sampling (NGS) is developed. First, we construct the local graph of each point using its K nearest neighbors according to the Euclidean distance metric; Second, for each local graph, its graph center point expands associated feature attribute by aggregating neighbor weights via point-wise dynamic filter; We then perform attention-based sampling to select a subset of points to well represent input points. The proposed NGS is embedded into an end-to-end analysis/synthesis-based variational autoencoder (VAE), with which the encoder applies multiscale NGS to extract latent keypoints that are augmented with neighbor structures and compressed at bottleneck leveraging the hyperpriors for accurate entropy modeling, and the decoder directly uses layered convolutions to refine progressively for the reconstruction of final point cloud. Note that all computations are fulfilled using point-wise convolution, making our solution an attractive approach in practice. Experimental results demonstrate that the proposed method using NGS mechanism outperforms the state-of-the-art point-based PCG compression methods by more than $2\times \mathrm{B}\mathrm{D}$-Rate (Bjûntegaard Delta Rate) gains, and several orders of magnitude gains over the MPEG G-PCC across all testing categories on ShapeNetCorev2 dataset.