  Recently, numerous learning-based compression methods have been developed
with outstanding performance for the coding of the geometry information of
point clouds. On the contrary, limited explorations have been devoted to point
cloud attribute compression (PCAC). Thus, this study focuses on the PCAC by
applying sparse convolution because of its superior efficiency for representing
the geometry of unorganized points. The proposed method simply stacks sparse
convolutions to construct the variational autoencoder (VAE) framework to
compress the color attributes of a given point cloud. To better encode latent
elements at the bottleneck, we apply the adaptive entropy model with the joint
utilization of hyper prior and autoregressive neighbors to accurately estimate
the bit rate. The qualitative measurement of the proposed method already rivals
the latest G-PCC (or TMC13) version 14 at a similar bit rate. And, our method
shows clear quantitative improvements to G-PCC version 6, and largely
outperforms existing learning-based methods, which promises encouraging
potentials for learnt PCAC.
