3D point cloud is one of the most common and basic 3D object representation model that is widely used in virtual/augmented reality applications, e.g., immersive communication. Compression of 3D point cloud is a big challenge because of its huge data volume and irregular data structure. In this paper, we propose a sampling-based compression algorithm for 3D point clouds. First, a 3D point cloud was resampled by a graph filter to obtain a subset of representative 3D points. Then, the representative points were compressed by the G-PCC (geometry-based point cloud compression) encoder software that was released by MPEG. Finally, the decoded representative points were used to reconstruct the original 3D point clouds by a CNN-based up-sampling approach. Experimental results demonstrate that a significant (73.15%) bit rate reduction can be achieved by the proposed 3D point cloud compression algorithm with minimal quality degradation of the reconstructed 3D point clouds.

3D点云是最常见和最基本的3D对象表示模型之一，已广泛用于虚拟/增强现实应用程序（例如，沉浸式通信）中。3D点云的压缩由于其庞大的数据量和不规则的数据结构而成为一个巨大的挑战。在本文中，我们提出了一种基于采样的3D点云压缩算法。首先，通过图形滤波器对3D点云进行重新采样，以获得代表3D点的子集。然后，代表点由MPEG发布的G-PCC（基于几何的点云压缩）编码器软件压缩。最后，解码后的代表点通过基于CNN的上采样方法用于重建原始3D点云。实验结果表明，这一点很重要（73。