Point cloud based immersive media representation format has provided many opportunities for extended reality applications and has become widely used in volumetric content capturing scenarios. The high data rate of the point cloud is one of the key problems preventing the adoption of this media format. MPEG Immersive media working group (MPEG-I) aims to create a point cloud compression methodology relying on the existing video coding hardware implementations to solve this problem. However, in the scope of the state-of-the-art video-based dynamic point cloud compression (V-PCC) standard under MPEG-I, the intrinsic 3D object's motion continuity is destroyed by the 2D projections resulting in a significant loss of inter prediction coding efficiency. In this paper, we first propose a general model utilizing the 3D motion and 3D to 2D correspondence to calculate the 2D motion vector (MV). Then under the V-PCC, we propose a geometry-based method using the accurate 3D reconstructed geometry from the 2D geometry video to estimate the 2D MV in the 2D attribute video. In addition, we propose an auxiliary-information-based method using the coarse 3D reconstructed geometry provided by the auxiliary information to estimate the 2D MV in both the 2D geometry and attribute videos. Furthermore, we provide the following two ways to use the estimated 2D MV to improve the coding efficiency. The first one is normative. We propose adding the estimated MV into the advanced motion vector candidate list and find a better motion vector predictor for each prediction unit (PU). The second one is non-normative. We propose applying the estimated MV as an additional candidate of the centers for motion estimation. We implement the proposed algorithms in the V-PCC reference software. The experimental results show that the proposed methods present significant coding gains compared with the current state-of-the-art motion prediction algorithm.

中文翻译：

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用于基于视频的动态点云压缩的高级3D运动预测。

基于点云的沉浸式媒体表示格式为扩展现实应用程序提供了许多机会，并已广泛用于体积内容捕获方案中。点云的高数据速率是阻止采用这种媒体格式的关键问题之一。MPEG浸入式媒体工作组（MPEG-I）旨在基于现有的视频编码硬件实现来创建点云压缩方法来解决此问题。但是，在MPEG-I下基于最新视频的动态点云压缩（V-PCC）标准的范围内，固有3D对象的运动连续性会被2D投影破坏，从而导致显着的图像丢失。帧间预测编码效率。在本文中，我们首先提出一个利用3D运动和3D与2D对应关系来计算2D运动矢量（MV）的通用模型。然后在V-PCC下，我们提出了一种基于几何的方法，该方法使用了来自2D几何视频的准确3D重构几何来估计2D属性视频中的2D MV。此外，我们提出了一种基于辅助信息的方法，该方法使用辅助信息提供的粗略3D重构几何来估计2D几何和属性视频中的2D MV。此外，我们提供了以下两种方式来使用估计的2D MV来提高编码效率。第一个是规范性的。我们建议将估计的MV添加到高级运动矢量候选列表中，并为每个预测单元（PU）找到更好的运动矢量预测因子。第二个是非规范性的。我们建议将估计的MV用作运动估计中心的其他候选者。我们在V-PCC参考软件中实现提出的算法。实验结果表明，与当前最新的运动预测算法相比，所提出的方法具有显着的编码增益。