Geometry Reconstruction for Spatial Scalability in Point Cloud Compression Based on the Prediction of Neighbours' Weights

Abstract

Spatial scalability is a critical feature in geometrybased point cloud compression (G-PCC). The current design of geometry reconstructions for spatial scalability applies points in fixed positions (center of nodes) and ignores the connection of points in regions. This work analyses the correlation between neighbours' occupancy and locally optimal reconstruction points within a node using the Pearson Product Moment Correlation Coefficient (PPMCC). Then we propose a geometry reconstruction method based on predicting the neighbours' weights. Geometry reconstruction points are calculated by applying weights inverse to distance to different categories of neighbours (face neighbours, edge neighbours, corner neighbours). Compared to the state-of-the-art G-PCC, performance improvement of 1.03dB in D1-PSNR and 2.90dB in D2-PSNR, on average, can be observed using the proposed method. Meanwhile, a simplified method is available to satisfy different complexity requirements.