# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 1 Introduction  
Multimedia is one of the most influential technological fields today. With the continuous advancement of multimedia technology, people are paying more and more attention to the visual experience. The introduction of advanced sensors has paved the way for the widespread adoption of 3D visual representation models. The digitization of 3D space allows for the exploration of content from any angle [1; 2; 3; 4; 5]. In addition, numerous innovative models for visual representation have emerged, particularly the video point cloud. A video point cloud consists of frames of point cloud data, which are collections of points in space. Each point is composed of coordinate and attribute information (e.g., texture, reflectance, color, and normal). Because video point clouds are more detailed than 2D representations, they contain a larger amount of data. Point cloud data in its raw format requires a significant amount of memory space and bandwidth [6]. Even under the conditions of fifth-generation mobile communication technology (5G), if no compression operation is performed, for an uncompressed DPC with one million points in each frame, its bitrate will reach up to 180 MB/s if its frame rate is 30 fps [7]. This presents significant challenges for data transmission and storage. Therefore, point cloud compression has become a crucial task. Point cloud compression can effectively reduce the amount of data, lower transmission costs, and preserve the integrity of key information, making point cloud technology more widely used in practice. In the fields of virtual reality, augmented reality, autonomous driving, 3D reconstruction, and object detection, point cloud compression can achieve more efficient data transmission and storage, thereby providing a more realistic and immersive experience. Therefore, there is a strong demand for effective point cloud compression technology in various industries [8; 9; 10; 11; 12; 13]. Its importance and application prospects cannotbe ignored, and it is of great significance for technological development and practical application. MPEG has established standards, including Geometric-based Point Cloud Compression (G-PCC) [14; 15], and Video-based Point Cloud Compression (V-PCC) [16; 17]. For G-PCC, the concept is to encode the content directly in 3D space. For V-PCC, MPEG published the Final Draft International Standard for V-PCC in 2020 [18]. In the draft, the point cloud is projected onto a 2D grid. This idea allows the traditional video compression algorithm to be used for point cloud compression. It achieves the objective of converting 3D data into 2D data for compression and subsequent recovery. MPEG has also developed the corresponding software, test model for category 2 (TMC2), which is still being updated. V-PCC has a high compression ratio with the assistance of a 2D video codec, enabling efficient processing of video point cloud data [19]. Therefore, this paper focuses on V-PCC. Although V-PCC has achieved point cloud compression, it also has some limitations. In addition to the 2D video coding load, the process of generating patches through the orthogonal projection of 3D points has the highest computational complexity in the V-PCC encoder. It has been found that refined segmentation treats each voxel equally, resulting in a significantly time-consuming process. At the same time, the size of the generated occupancy map during the point cloud compression process is large, which will result in a decrease in the performance of point cloud compression. To address these issues, we have made enhancements in the refining segmentation and patch packing. The main contributions of this paper can be summarized as follows:  
* Addressing the issue of a high time-consuming ratio of the refining segmentation module in the patch generation process, we propose the Voxel Selection-based Refining Segmentation (VS-RS).