Point Cloud Geometry Compression via Density-Constrained Adaptive Graph Convolution

Abstract

Recently, point-based point cloud geometry compression has attracted great attention due to its superior performance at low bit rates. However, lacking an efficient way to represent the local geometric correlation well, most existing methods [1, 2, 3] can hardly extract fine local features accurately. Thus it's difficult for them to obtain high-quality reconstruction of local geometry of point clouds.