In this article, a survey of the point cloud compression (PCC) methods by organizing them with respect to the data structure, coding representation space, and prediction strategies is presented. Two paramount families of approaches reported in the literature-the projection- and octree-based methods-are proven to be efficient for encoding dense and sparse point clouds, respectively. These approaches are the pillars on which the Moving Picture Experts Group Committee developed two PCC standards published as final international standards in 2020 and early 2021, respectively, under the names: video-based PCC and geometry-based PCC. After surveying the current approaches for PCC, the technologies underlying the two standards are described in detail from an encoder perspective, providing guidance for potential standard implementors. In addition, experiment evaluations in terms of compression performances for both solutions are provided.

For a V-PCC1system to be able to reconstruct a single instance of the point cloud one V-PCC unit must be transferred to the 3D point cloud reconstruction module. It is however required that all the V-PCC components i.e. occupancy map, geometry, atlas and attribute to be temporally aligned. This, in principle, could pose a challenge since the temporal structures of the decoded sub-bitstreams are not coherent across V-PCC sub-bitstreams. In this paper we propose an output delay adjustment mechanism for the decoded V-PCC sub-bitstreams to provide synchronized V-PCC components input to the point cloud reconstruction module.

Point cloud based immersive media representation format has provided many opportunities for extended reality applications and has become widely used in volumetric content capturing scenarios. The high data rate of the point cloud is one of the key problems preventing the adoption of this media format. MPEG Immersive media working group (MPEG-I) aims to create a point cloud compression methodology relying on the existing video coding hardware implementations to solve this problem. However, in the scope of the state-of-the-art video-based dynamic point cloud compression (V-PCC) standard under MPEG-I, the intrinsic 3D object's motion continuity is destroyed by the 2D projections resulting in a significant loss of inter prediction coding efficiency. In this paper, we first propose a general model utilizing the 3D motion and 3D to 2D correspondence to calculate the 2D motion vector (MV). Then under the V-PCC, we propose a geometry-based method using the accurate 3D reconstructed geometry from the 2D geometry video to estimate the 2D MV in the 2D attribute video. In addition, we propose an auxiliary-information-based method using the coarse 3D reconstructed geometry provided by the auxiliary information to estimate the 2D MV in both the 2D geometry and attribute videos. Furthermore, we provide the following two ways to use the estimated 2D MV to improve the coding efficiency. The first one is normative. We propose adding the estimated MV into the advanced motion vector candidate list and find a better motion vector predictor for each prediction unit (PU). The second one is non-normative. We propose applying the estimated MV as an additional candidate of the centers for motion estimation. We implement the proposed algorithms in the V-PCC reference software. The experimental results show that the proposed methods present significant coding gains compared with the current state-of-the-art motion prediction algorithm.

Point cloud media representation format has provided various opportunities for extended reality applications and had become widely used in volumetric content capturing scenarios. At the same time ambiguous storage format representations and network throughput are key problems for wide adoption of this media format. Compression algorithms in corresponding standard activities are aimed to solve this problem. MPEG-I standard has an aim of creating the point cloud compression methodology relying on existing video coding hardware implementations. In scope of the state-of-the-art video-based dynamic point cloud (DPC) compression method, similar 3D patches may be projected in totally different 2D positions in different frames. In this way, the motion vector predictors especially those in the patch boundary may be very inaccurate which may lead to significant bitrate increase. In this paper, we propose to use the reconstructed geometry information to help predict the motion vector more accurately and improve the coding efficiency of the attribute video. First, we propose to use the motion vector of the co-located blocks in the geometry frame as a merge candidate of the current block in the attribute frame. Second, we perform a motion estimation between the current reconstructed point cloud with only the geometry information and the reference point cloud to find the corresponding block. The motion information derived is used as motion vector predictor of the current block in the attribute frame. As far as we can see, this is the first work using the geometry information to compress the attribute in the DPC compression scenario. Significant compression efficiency is achieved with this new 3D point cloud geometry derived motion prediction scheme when compared with the state-of-the-art DPC compression method.

Point clouds are one of the emerging 3D visual representations of real word and plenty of useful applications has already been demonstrated. However, a huge amount of data associated with it has added challenges in both transmission and storage. This requires an efficient coding solution and brought a great attention among compression community. MPEG and JPEG standardization group has already started developing coding solution and proposed two test-models namely V-PCC, video-based coding solution, for dynamic point cloud and G-PCC, a native geometry-based coding solution, for static and LiDAR point cloud. In G-PCC, octree (lossless) and tri-soup(lossy) for geometry coding, similarly regional adaptive hierarchical transform (RAHT) and lifting-scheme for attributes coding are currently being explored. Lifting-scheme relies on level-of-details(LOD) structure for attributes prediction where LOD is generated with distance based subsampling approach. In this work we proposed a new LOD generation scheme using binary-tree and showed it provides better coding solution for sparse point cloud such as LiDAR. The experimental results demonstrated 12% bitrate reduction for reflectance and 8%, 6% and 7% bitrate reduction for luma, chroma Cb and chroma Cr respectively as well as up to 4 times computational complexity reduction compared to current G-PCC lifting-scheme.

Widespread of virtual reality technologies across entertainment formats has created a diverse infrastructure of related technologies as head-mount displays, dome screens and virtual reality multi-camera platforms. As omnidirectional content is processing pipeline is completely different form conventional planar video and involves multiple conversion steps which affect quality in a different way. As a result of our research we propose objective quality estimation methodology and a set of tools to evaluate different projection methods and coding tools for omnidirectional video content.