论文标题
基于学习的无损点云几何形状使用稀疏张量编码
Learning-based Lossless Point Cloud Geometry Coding using Sparse Tensors
论文作者
论文摘要
大多数点云压缩方法在体voxel或OctRee域中运行，这不是点云的原始表示。这些表示形式要么删除几何信息，要么需要高计算能力来处理。在本文中，我们提出了一个基于上下文的无损点云几何压缩，该压缩直接处理点表示。在点表示上操作使我们能够保持点之间的几何相关性，从而获得准确的上下文模型，同时显着降低计算成本。具体而言，我们的方法使用稀疏的卷积神经网络从X，Y，Z输入数据依次估算体素占用率。实验结果表明，我们的方法的表现优于MPEG的最先进的几何压缩标准，而从四个不同数据集的一组点云中，平均率节省了52％。
Most point cloud compression methods operate in the voxel or octree domain which is not the original representation of point clouds. Those representations either remove the geometric information or require high computational power for processing. In this paper, we propose a context-based lossless point cloud geometry compression that directly processes the point representation. Operating on a point representation allows us to preserve geometry correlation between points and thus to obtain an accurate context model while significantly reduce the computational cost. Specifically, our method uses a sparse convolution neural network to estimate the voxel occupancy sequentially from the x,y,z input data. Experimental results show that our method outperforms the state-of-the-art geometry compression standard from MPEG with average rate savings of 52% on a diverse set of point clouds from four different datasets.
