论文标题
对解压缩点云的深度几何后期处理
Deep Geometry Post-Processing for Decompressed Point Clouds
论文作者
论文摘要
点云压缩在减少数据存储和传输的巨大成本中起着至关重要的作用。但是，由于量化，可以将失真引入解压缩点云中。在本文中，我们提出了一种基于学习的新型后处理方法，以增强解压缩点云。具体而言，首先将Voxelized点云分为小立方体。然后，提出了一个3D卷积网络，以预测立方体每个位置的占用概率。我们通过产生多规模概率来利用本地和全球环境。这些概率逐渐求和，以粗到1的方式预测结果。最后，我们根据预测的概率获得了几何形状的点云。与以前的方法不同，我们使用单个模型处理具有多种变形的解压缩点云。实验结果表明，所提出的方法可以显着提高压缩点云的质量，从而平均在三个代表性数据集上实现9.30dB BDPSNR增益。
Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission. However, distortions can be introduced into the decompressed point clouds due to quantization. In this paper, we propose a novel learning-based post-processing method to enhance the decompressed point clouds. Specifically, a voxelized point cloud is first divided into small cubes. Then, a 3D convolutional network is proposed to predict the occupancy probability for each location of a cube. We leverage both local and global contexts by generating multi-scale probabilities. These probabilities are progressively summed to predict the results in a coarse-to-fine manner. Finally, we obtain the geometry-refined point clouds based on the predicted probabilities. Different from previous methods, we deal with decompressed point clouds with huge variety of distortions using a single model. Experimental results show that the proposed method can significantly improve the quality of the decompressed point clouds, achieving 9.30dB BDPSNR gain on three representative datasets on average.
