  Octree-based point cloud representation and compression have been adopted by
the MPEG G-PCC standard. However, it only uses handcrafted methods to predict
the probability that a leaf node is non-empty, which is then used for entropy
coding. We propose a novel approach for predicting such probabilities for
geometry coding, which applies a denoising neural network to a "noisy" context
cube that includes both neighboring decoded voxels as well as uncoded voxels.
We further propose a convolution-based model to upsample the decoded point
cloud at a coarse resolution on the decoder side. Integration of the two
approaches significantly improves the rate-distortion performance for geometry
coding compared to the original G-PCC standard and other baseline methods for
dense point clouds. The proposed octree-based entropy coding approach is
naturally scalable, which is desirable for dynamic rate adaptation in point
cloud streaming systems.
