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Abstract：
A point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual reality and augmented reality. However, the point cloud, especially those representing dynamic scenes or objects in motion, must be compressed efficiently due to its huge data volume. The latest video-based point cloud compression (V-PCC) standard for dynamic point clouds divides the 3D point cloud into many patches using computationally expensive normal estimation, segmentation, and refinement. The patches are projected onto a 2D plane to apply existing video coding techniques. This process often results in losing proximity information and some original points. This loss induces artefacts that adversely affect user perception. The proposed method segments dynamic point clouds based on shape similarity and occlusion before patch generation. This segmentation strategy helps maintain the points\' proximity and retain more original points by exploiting the density and occlusion of the points. The experimental results establish that the proposed method significantly outperforms the V-PCC standard and other relevant methods regarding rate-distortion performance and subjective quality testing for both geometric and texture data of several benchmark video sequences.
摘要：
点云是利用包括3D位置和属性的无序点的对象或场景的表示。点云模仿自然形态的能力已获得不同应用领域的极大关注，例如虚拟现实和增强现实。然而,点云,尤其是那些代表动态场景或运动物体的人，由于其巨大的数据量，必须有效地压缩。用于动态点云的最新基于视频的点云压缩（V-PCC）标准使用计算昂贵的正态估计将3D点云划分为许多补丁，分割，和细化。面片被投影到2D平面上以应用现有的视频编码技术。该过程通常导致丢失接近度信息和一些原始点。这种损失引起对用户感知产生不利影响的伪像。该方法在块生成前基于形状相似性和遮挡分割动态点云。这种分割策略通过利用点的密度和遮挡来帮助保持点的接近度并保留更多的原始点。实验结果证明，对于几个基准视频序列的几何和纹理数据，该方法在率失真性能和主观质量测试方面明显优于V-PCC标准和其他相关方法。
