标题： 基于视频的光场点云压缩 显示英文标题

标题： Video-based compression for plenoptic point clouds

摘要： 全光点云从不同方向具有多种颜色，比通常只有一种颜色的一般点云表示更加完整。 它更加真实，但也带来了需要高效压缩的大量数据。 压缩全光点云的最先进方法是对基于区域的自适应分层变换（RAHT）的扩展。 据我们所知，除了RAHT之外，基于视频的点云压缩（V-PCC）也是一种高效的点云压缩方法。 然而，据我们所知，目前还没有工作使用基于视频的解决方案来压缩全光点云。 在本文中，我们首先通过生成多个属性视频来扩展V-PCC以支持全光点云压缩。 然后，基于从多个视图生成的这些视频具有非常高的相关性的观察，我们提出使用多视图高效视频编码对其进行编码。 我们进一步提出了一种基于块的填充方法，将来自不同视图的未占用属性像素统一起来，以减少它们的比特率成本。 所提出的算法在V-PCC参考软件中实现。 实验结果表明，与全光点云压缩的最先进方法相比，所提出的算法可以带来显著的比特率节省。 显示英文摘要

摘要： The plenoptic point cloud that has multiple colors from various directions, is a more complete representation than the general point cloud that usually has only one color. It is more realistic but also brings a larger volume of data that needs to be compressed efficiently. The state-of-the-art method to compress the plenoptic point cloud is an extension of the region-based adaptive hierarchical transform (RAHT). As far as we can see, in addition to RAHT, the video-based point cloud compression (V-PCC) is also an efficient point cloud compression method. However, to the best of our knowledge, no works have used a video-based solution to compress the plenoptic point cloud yet. In this paper, we first extend the V-PCC to support the plenoptic point cloud compression by generating multiple attribute videos. Then based on the observation that these videos from multiple views have very high correlations, we propose encoding them using multiview high efficiency video coding. We further propose a block-based padding method that unifies the unoccupied attribute pixels from different views to reduce their bit cost. The proposed algorithms are implemented in the V-PCC reference software. The experimental results show that the proposed algorithms can bring significant bitrate savings compared with the state-of-the-art method for plenoptic point cloud compression.