[AI-3DGC] NF-PCAC - deep point cloud attribute compression with normalizing flow
Résumé
There exist different learning-based approaches to compress the geometry of point clouds, on the other hand the compression of attributes has been less explored. In this contribution we present a first normalizing flow architecture to compress point cloud attributes and the strategies to enable its use. The proposed method outperforms all other learning-based methods for point cloud attributes in the proposed quantitative measurement and has comparable results to G-PCC version 14 on some test models.