论文标题
横截面方法的动态点云压缩
Dynamic Point Cloud Compression with Cross-Sectional Approach
论文作者
论文摘要
动态点云的最新发展引入了模仿自然现实的可能性，并极大地帮助生活质量。但是，为了成功广播，与传统视频相比，由于其大量数据，动态点云需要更高的压缩。最近，MPEG最终确定了一个基于视频的点云压缩标准，即V-PCC。但是，由于昂贵的正常计算和分割，V-PCC需要巨大的计算时间，牺牲了一些点以限制2D补丁的数量，并且不能占据2D帧中的所有空间。提出的方法通过使用新型的横截面方法来解决这些局限性。与VPCC相比，这种方法可减少昂贵的正常估计和分割，保留更多的点，并利用更多的2D框架生成空间。使用标准视频序列的实验结果表明，与V-PCC标准相比，所提出的技术可以在几何和纹理数据中获得更好的压缩。
The recent development of dynamic point clouds has introduced the possibility of mimicking natural reality, and greatly assisting quality of life. However, to broadcast successfully, the dynamic point clouds require higher compression due to their huge volume of data compared to the traditional video. Recently, MPEG finalized a Video-based Point Cloud Compression standard known as V-PCC. However, V-PCC requires huge computational time due to expensive normal calculation and segmentation, sacrifices some points to limit the number of 2D patches, and cannot occupy all spaces in the 2D frame. The proposed method addresses these limitations by using a novel cross-sectional approach. This approach reduces expensive normal estimation and segmentation, retains more points, and utilizes more spaces for 2D frame generation compared to the VPCC. The experimental results using standard video sequences show that the proposed technique can achieve better compression in both geometric and texture data compared to the V-PCC standard.
