DDA-Net: Deep Distribution-Aware Network for Point Cloud Compression

Abstract

Deep neural networks have been recently applied to point cloud compression (PCC). The features extracted via deep neural networks are essential for compression performance. Different from high level tasks such as point cloud classification or segmentation which homogenizes descriptors within same classes, PCC requires low level features discriminative for point-level 3D reconstructions. With this motivation, we first adopt Gaussian distribution to model the shape of feature elements. Then, we propose a deep distribution-aware network (DDA-Net) which manipulates distributions of feature elements on-the-fly to favor the point cloud reconstruction with high fidelity. Moreover, a residual network is integrated to enhance the modification of the Gaussian models. The proposed DDA-Net is incorporated into an end-to-end PCC system. Experimental results show that our DDA-Net significantly improves the compression performance across a wide range of point clouds.