In this golden age of multimedia, realistic content is in high demand with users seeking more immersive and interactive experiences. As a result, new image modalities for 3D representations have emerged in recent years, among which point clouds have deserved especial attention. Naturally, with this increase in demand, efficient storage and transmission became a must, with standardization groups such as MPEG and JPEG entering the scene, as it happened before with other types of visual media. In a surprising development, JPEG issued a Call for Proposals on point cloud coding targeting exclusively learning-based solutions, in parallel to a similar call for image coding. This is a natural consequence of the growing popularity of deep learning, which due to its excellent performances is currently dominant in the multimedia processing field, including coding. This paper presents the coding solution selected by JPEG as the best-performing response to the Call for Proposals and adopted as the first version of the JPEG Pleno Point Cloud Coding Verification Model, in practice the first step for developing a standard. The proposed solution offers a novel joint geometry and color approach for point cloud coding, in which a single deep learning model processes both geometry and color simultaneously. To maximize the RD performance for a large range of point clouds, the proposed solution uses down-sampling and learning-based super-resolution as pre- and post-processing steps. Compared to the MPEG point cloud coding standards, the proposed coding solution comfortably outperforms G-PCC, for both geometry, color, and joint quality metrics.

Deep learning (DL) based tools have recently reached performance levels similar to state-of-the-art hand-crafted methods for Point Cloud (PC) coding and classification. In 2022, JPEG issued a Call for Proposals for a Learning-based PC Coding (PCC) standard that envisions a unified representation, targeting both human visualization and computer vision tasks. This paper proposes the first DL-based Compressed Domain PC CLassifier (CD-PCCL), built on the PointGrid classifier, for geometry-only PCs coded with the current DL-based JPEG Pleno PCC Verification Model. The performance of compressed domain PC classification is studied against using voxel domain classification, notably for original, voxelized, and decompressed PCs. Experimental results with the ModelNet40 PC dataset show the proposed CD-PCCL can achieve significant PC classification gains regarding decompressed domain classification, while reducing the PC classifier complexity.

Deep learning (DL)-based point cloud (PC) classification is a key computer vision task for many applications, notably autonomous driving, surveillance, and cultural heritage. In many application scenarios, PCs must be coded to reach practical rates for storage and transmission purposes, and thus they suffer from more or less intense compression artifacts. After the specification of two MPEG PC coding standards, DL-based PC coding has gained momentum, reaching competitive compression performance, especially for dense PCs. Since using decoded PCs, which may suffer from compression artifacts, may impact the final classification performance, the main goal of this paper is to study the impact of static PC geometry coding on DL-based classification. This study is performed on the ModelNet40 test dataset using the conventional G-PCC coding standard and the DL-based PC geometry codec which was the top performing solution responding to the recent JPEG Pleno PC Coding Call for Proposals. Two highly performing DL-based classifiers are used, considering the original PC geometry before and after voxelization, as well as the decoded PC geometry for different rates and qualities. As expected, coding has an impact on the classification performance, especially for the lower rates/qualities. For very sparse PCs, conventional coding still has advantage, contrarily to dense PCs, but this should change in the future with DL-based tools becoming the most natural solutions for both PC geometry coding and classification.