Sparse Convolution Based Point Cloud Attributes Deblocking with Graph Fourier Latent Representation

The rapid development of 3D modeling and computer vision has made point cloud data essential across various industries. Effective processing, transmission, and storage of these point clouds require innovative filtering and compression methods. Unlike traditional image and video media, point clouds are sparse and non-uniformly sampled, posing unique compression challenges. In this paper, we introduce a novel approach for deblocking and denoising point clouds using multi-scale sparse convolution-based attribute learning in the spectral domain. Our method leverages concurrent voxelized feature embedding for efficiency and utilizes sparsity to build a deeper learning structure. By employing the Graph Fourier Transform (GFT), we better understand spectral patterns and spatial correlations of attributes. Experiments show that our GFT latent representation significantly enhances reconstruction quality, achieving an 18.5% Y-BD rate reduction compared to GPCC TMC13v14 anchors. This extends our previous work, MUSCON, the first SparseConv-based multi-scale attribute upsampling solution for deblocking.