Tohidi Faranak, Paul Manoranjan, Ulhaq Anwaar, Chakraborty Subrata
School of Computing Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia.
School of Engineering and Technology, Centre for Intelligent Systems, Central Queensland University, Sydney Campus, Rockhampton, QLD 4701, Australia.
Sensors (Basel). 2024 Jul 1;24(13):4285. doi: 10.3390/s24134285.
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.
点云是一种利用包含3D位置和属性的无序点来表示物体或场景的方式。点云模拟自然形态的能力已在虚拟现实和增强现实等多个应用领域引起了广泛关注。然而，由于点云数据量巨大，特别是那些表示动态场景或运动物体的点云，必须进行高效压缩。最新的基于视频的动态点云压缩（V-PCC）标准使用计算成本高昂的法线估计、分割和细化将3D点云划分为许多面片。这些面片被投影到2D平面上以应用现有的视频编码技术。这个过程常常会导致丢失邻近信息和一些原始点。这种丢失会产生伪像，对用户感知产生不利影响。所提出的方法在生成面片之前基于形状相似性和遮挡对动态点云进行分割。这种分割策略通过利用点的密度和遮挡来帮助保持点的邻近性并保留更多原始点。实验结果表明，在所测试的几个基准视频序列的几何和纹理数据的率失真性能和主观质量测试方面，所提出的方法明显优于V-PCC标准和其他相关方法。
