MUSCON: Multi-scale Sparse Conv Learning for Point Cloud Attributes Deblocking

High-fidelity 3D representations of objects and scenes can be obtained with point clouds, but dealing with their massive data sizes can be difficult. This data is efficiently compressed via MPEG's Geometry-based Point Cloud Compression (G-PCC), which makes it manageable and useful for real-world applications. One major drawback, though, is that decoding introduces coding artifacts that cause the reconstructed point cloud to appear blocky. In this paper, we present a new approach to attribute learning in point clouds leveraging sparse convolution, that effectively deals with the non-uniformity and sparsity of these data structures.