论文标题
基于学习的3D点云几何形状的无损压缩
Learning-based lossless compression of 3D point cloud geometry
论文作者
论文摘要
本文基于上下文自适应算术编码提供了一种基于学习的，无损的压缩方法，用于静态点云几何形状。与在OCTREE域中使用的大多数现有方法不同，我们的编码器以混合模式运行，将OCTREE和基于体素的编码混合。我们根据点云结构将点云适应为多分辨率体素块，并使用OCTREE来发出分区信号。一方面，OCTREE表示可以消除点云中的稀疏性。另一方面，在体素域中，可以自然表达卷积，并且神经网络明确处理了几何信息（即平面，表面等）。我们的上下文模型从这些属性中受益，并使用带有掩盖过滤器的深卷积神经网络（称为Voxeldnn）了解体素的概率分布。实验表明，我们的方法优于最先进的MPEG G-PCC标准，平均利率为28％的平均率在来自Microsoft Voxelized上体（MVUB）和MPEG的各种点云上。该实现可在https://github.com/weafre/voxeldnn上获得。
This paper presents a learning-based, lossless compression method for static point cloud geometry, based on context-adaptive arithmetic coding. Unlike most existing methods working in the octree domain, our encoder operates in a hybrid mode, mixing octree and voxel-based coding. We adaptively partition the point cloud into multi-resolution voxel blocks according to the point cloud structure, and use octree to signal the partitioning. On the one hand, octree representation can eliminate the sparsity in the point cloud. On the other hand, in the voxel domain, convolutions can be naturally expressed, and geometric information (i.e., planes, surfaces, etc.) is explicitly processed by a neural network. Our context model benefits from these properties and learns a probability distribution of the voxels using a deep convolutional neural network with masked filters, called VoxelDNN. Experiments show that our method outperforms the state-of-the-art MPEG G-PCC standard with average rate savings of 28% on a diverse set of point clouds from the Microsoft Voxelized Upper Bodies (MVUB) and MPEG. The implementation is available at https://github.com/Weafre/VoxelDNN.
