Photo-realistic point cloud capture and transmission are the fundamental enablers for immersive visual communication. The coding process of dynamic point clouds, especially video-based point cloud compression (V-PCC) developed by the MPEG standardization group, is now delivering state-of-the-art performance in compression efficiency. V-PCC is based on the projection of the point cloud patches to 2D planes and encoding the sequence as 2D texture and geometry patch sequences. However, the resulting quantization errors from coding can introduce compression artifacts, which can be very unpleasant for the quality of experience (QoE). In this work, we developed a novel out-of-the-loop point cloud geometry artifact removal solution that can significantly improve reconstruction quality without additional bandwidth cost. Our novel framework consists of a point cloud sampling scheme, an artifact removal network, and an aggregation scheme. The point cloud sampling scheme employs a cube-based neighborhood patch extraction to divide the point cloud into patches. The geometry artifact removal network then processes these patches to obtain artifact-removed patches. The artifact-removed patches are then merged together using an aggregation scheme to obtain the final artifact-removed point cloud. We employ 3D deep convolutional feature learning for geometry artifact removal that jointly recovers both the quantization direction and the quantization noise level by exploiting projection and quantization prior. The simulation results demonstrate that the proposed method is highly effective and can considerably improve the quality of the reconstructed point cloud.

中文翻译：

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基于视频的点云压缩伪影去除

逼真的点云捕获和传输是沉浸式视觉交流的基本推动因素。动态点云的编码过程，尤其是由 MPEG 标准化组开发的基于视频的点云压缩 (V-PCC)，现在正在提供最先进的压缩效率性能。V-PCC 基于点云块到 2D 平面的投影，并将序列编码为 2D 纹理和几何块序列。然而，编码产生的量化误差会引入压缩伪影，这对体验质量 (QoE) 来说可能非常令人不快。在这项工作中，我们开发了一种新颖的循环外点云几何伪影去除解决方案，可以在不增加带宽成本的情况下显着提高重建质量。我们的新框架由点云采样方案、工件去除网络和聚合方案组成。点云采样方案采用基于立方体的邻域补丁提取将点云划分为补丁。然后几何工件去除网络处理这些补丁以获得去除工件的补丁。然后使用聚合方案将去除工件的补丁合并在一起以获得最终去除工件的点云。我们采用 3D 深度卷积特征学习来去除几何伪影，通过利用投影和量化先验来联合恢复量化方向和量化噪声水平。仿真结果表明，该方法非常有效，可以显着提高重建点云的质量。