# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 2 Related Work  
Figure 1 illustrates the encoding process of TMC2 developed based on V-PCC mainly includes four steps: patch generation, patch packaging, image generation, and video encoding.  
Patches generation aims to utilize the optimal segmentation strategy for projecting 3D data onto a specified plane and decomposing point cloud frames by converting them into 2D data. In TMC2, the objective of patch generation is to divide the point cloud into groups of patches with smooth boundaries, while also ensuring minimal reconstruction errors. The steps include principal component analysis to obtain the normal vector, initial segmentation of point clouds based on the normal vector, refining segmentation based on smoothing scores, and extraction of patches from connected components [6]. The area within the dashed line in Figure 1 represents the patch generation process. The goal of the patch packaging process is to generate geometric and texture images that contribute to video compression. During this process, auxiliary information, such as occupancy maps, will also be generated to indicate effective positions. The occupancy map size should be as small as possible, and the internal patches should be arranged tightly. The image generation process includes optimization operations such as geometric pixel filling, attribute pixel filling, and global allocation. Finally, all the generated images are combined into a video and encoded using HEVC [19] or other video encoders. This paper focuses on refining segmentation and patch-packing sessions. The details of these two parts will be presented in Section 3. A comprehensive description of the remaining components of V-PCC can be found in the literature [11].  
Even though MPEG has identified the latest standard, it still has some limitations. Researchers are attempting to enhance coding efficiency by employing various methods, including that of [PERSON] et al. [20; 21]. A grid-based refining segmentation (G-RS) method was proposed to reduce the complexity of a point-based refining segmentation method, and its performance was evaluated additionally in a dedicated core experiment [22]. Despite these efforts, the G-RS procedure still accounted for a large time consuming of the V-PCC encoders [23]. Later, [PERSON] et al. [24] proposed an optimization method to avoid unnecessary hash map access. [PERSON] 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 RGRS algorithm. However, it brings other problems such as a decrease in the quality of point cloud reduction.  
In addition to improving coding efficiency, researchers have also made improvements based on patches. [PERSON] et al. [27] estimate the most important patches of the second layer and incorporate them into the patch generation of the first layer. Compared to the single-layer scheme, the proposed method reduces the bit rate, but it also degrades the subjective quality of the point cloud after reduction. [PERSON] et al. [28] proposed to optimize the user's visual experience by allocating more patches related to predefined views and increasing codec time. [PERSON] et al. [29] have proposed a method for decreasing unoccupied pixels among different patches due to the inefficiency of coding unused space during video compression. [PERSON] et al. [30] proposed their method for obtaining a contextual homogeneity-based patch decomposition affecting compression efficiency. However, it does not work on additional attributes such as reflection and material. There are also some other similar methods [31; 32]. The idea of using patch packaging methods to improve occupancy maps is also worth studying.  
Figure 1: The process of V-PCC.