Point Cloud Compression (PCC) has received a lot of attention in recent years due to its wide applications such as virtual reality (VR), augmented reality (AR), and mixed reality (MR). Video-based PCC (V-PCC) and geometry-based PCC (G-PCC) are two distinct technologies developed by MPEG 3DG[1][2]. Deep-learning-based (DL-based) PCC is a strong competitor to them. Most DL methods generalize the DL-based image coding pipeline to the point cloud data [3][4]. They outperform G-PCC in the current MPEG 3DG standard in the dense point cloud compression. Yet, their performances are still inferior to that of V-PCC in the coding of dynamic point clouds.

We propose to design a learning-based PCC solution that could outperform those DL-based methods with lower complexity and less memory consumption. Our method uses geometry projection to generate 2D images and apply vector quantization-based 2D image codec to compress the projected map. For a point cloud sequence, we can do the projection in three steps. First, split the sequence into blocks by doing the octree partition. Second, project each 3D block into a plane and pack all the planes into a map. Third, encode/decode the 2D map and reconstruct the 3D point cloud sequence. They are demonstrated in Fig.1. We do the non-uniform sampling for the projected planes and pack all the planes to generate one depth map and one texture map in the reconstruction process. The two maps are shown in Fig.2.

Presently, we utilize the x264/x265 codec to code the maps. In the future, we will adopt a vector quantization-based image codec to compress the two maps.

— Qingyang Zhou


[1] S. Schwarz, M. Preda, V. Baroncini, M. Budagavi, P. Cesar, P. A. Chou, R. A. Cohen, M. Krivoku ́ca, S. Lasserre, Z. Li et al., “Emerging MPEG standards for point cloud compression,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 9, no. 1, pp. 133–148, 2019.

[2] M. D. Graphics, “Call for proposals for point cloud compression v2,” ISO/IEC JTC1/SC29/WG11 MPEG2017/N16763, 2017.

[3] Jianqiang Wang, Hao Zhu, Zhan Ma, Tong Chen, Haojie Liu, and Qiu Shen. Learned point cloud geometry compression. arXiv preprint arXiv:1909.12037, 2019.

[4] Wei Yan, Shan Liu, Thomas H Li, Zhu Li, Ge Li. Deep autoencoder-based lossy geometry compression for point clouds. arXiv preprint arXiv:1905.03691, 2019.