High-performance coding solutions are urgently needed for the storage and transmission of 3D point clouds due to the development of 3D data acquisition facilities and the increasing scale of acquired point clouds. Video-based point cloud compression (V-PCC) is the most advanced international standard for compressing dynamic point clouds. However, it still has serious issues of time consumption and the large size of the occupancy map. Considering the aforementioned issues, based on V-PCC, we propose the Voxel Selection-based Refining Segmentation (VS-RS), which is used to accelerate the refining segmentation process of the point cloud. Furthermore, the data-adaptive patch packing (DAPP) is proposed to reduce the size of the occupancy map. In order to specify the effect of the improvement, we also designed novel evaluation indicators. Experimental results show that the proposed method achieves a Bjøntegaard Delta rate (BD-rate) gain of −1.58% in the V-PCC benchmark. Additionally, it reduces encoding time by up to 31.86% and reduces the size of the occupancy map by up to 20.14%.

Due to the often substantial size of the real-world point cloud data, efficient transmission and storage have become critical concerns. Point cloud compression plays a decisive role in addressing these challenges. Recognizing the importance of capturing global information within point cloud data for effective compression, many existing point cloud compression methods overlook this crucial aspect. To tackle this oversight, we propose an innovative end-to-end point cloud compression method designed to extract both global and local information. Our method includes a novel Transformer module to extract rich features from the point cloud. Utilization of a pooling operation that requires no learnable parameters as a token mixer for computing long-distance dependencies ensures global feature extraction while significantly reducing both computations and parameters. Furthermore, we employ convolutional layers for feature extraction. These layers not only preserve the spatial structure of the point cloud, but also offer the advantage of parameter independence from the input point cloud size, resulting in a substantial reduction in parameters. Our experimental results demonstrate the effectiveness of the proposed TransPCGC network. It achieves average Bjontegaard Delta Rate (BD-Rate) gains of 85.79% and 80.24% compared to Geometry-based Point Cloud Compression (G-PCC). Additionally, in comparison to the Learned-PCGC network, our approach attains an average BD-Rate gain of 18.26% and 13.83%. Moreover, it is accompanied by a 16% reduction in encoding and decoding time, along with a 50% reduction in model size.