Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud Compression

Abstract

The video-based point cloud compression (V-PCC, ISO/IEC 23090-5) is the state-of-theart international standard for compressing dynamic point clouds developed by the moving picture experts group (MPEG). It has been achieved good rate-distortion (RD) performance by employing the 2D-based dynamic point cloud compression. As a brief look, V-PCC first converts the 3D input point cloud into a set of 2D patches followed by a packing process. The packing process then maps the patches into a 2D grid. Such a way allows compressing the patches utilizing the existing video coding standards. Besides the RD performance, complexity is another vital factor to consider in performance evaluations. In the V-PCC encoder, the self-time accounts for on average 15.9% and a maximum of 48.2% of the total-time, which can be a hindrance to realizing real-time V-PCC applications. One of the most computationally intensive modules of V-PCC is the grid-based refining segmentation (G-RS). Thus this paper proposes a fast G-RS method that can adaptively select voxels that need the refining segmentation. More concretely, the proposed method classifies the voxels based on the projection plane indices of 3D points and only applies the refining process to the selected voxels. Experimental results demonstrate that the proposed method reduces the complexity of the refining steps in G-RS, on average, by 60.7% and 62.5% without coding efficiency loss compared to the test model for category 2 (TMC2) version 12.0 reference software under the random access (RA) and all-intra (AI) configurations, respectively.INDEX TERMS video-based point cloud compression, dynamic point cloud, fast algorithms, low-complex video encoders, voxel-based refining segmentation, encoder optimization

Improved Patch Packing and Refining Segmentation for the V-PCC Standard

“…This paper aims to reduce time consumption while improving RD performance and compares the proposed algorithm with TMC2-v18.0 to demonstrate its advantages. We also compare with methods from [25,26,35]. Generally, the bit rates generated by various methods do not match perfectly.…”

“…Later, Higa et al [24] proposed an optimization method to avoid unnecessary hash map access. Kim et al [25,26] used a voxel classification algorithm to improve the refining segmentation process, and, on this basis, they proposed a radius search method for the FGRS algorithm. However, it brings other problems such as a decrease in the quality of point cloud reduction.…”

Improved video-based point cloud compression via segmentation

“…In contrast, geometry-based compression, G-PCC, encodes the content directly in the 3D space. While V-PCC applies the existing 2D video compression approach to a collection of various 2D pictures transformed from the 3D point data, GPCC applies Octree and K-D tree data structures to describe the placement of the points and their vicinities in the 3D space [28][29][30][31] . As V-PCC stands as the current state-of-the-art in dynamic point cloud compression, this section introduces recent literature focused on enhancing the efficiency of V-PCC:…”

Rate-Distortion Optimal Transform Coefficient Selection for Unoccupied Regions in Video-Based Point Cloud Compression

“…Other work focused on optimizing the framework for more efficient encoding. For example, it was proposed to simplify the grid-based refining segmentation to reduce processing times [10]. It was found that the compression performance in terms of rate-distortion costs did not suffer.…”