论文标题
用同胞上下文和表面先验的点云压缩
Point Cloud Compression with Sibling Context and Surface Priors
论文作者
论文摘要
我们为大规模点云压缩提供了一种新型的基于OCTREE的多层次框架，该框架可以以内存有效的方式组织稀疏和非结构化的点云。在此框架中，我们提出了一个新的熵模型，该模型使用兄弟姐妹的孩子，祖先和邻居的上下文探讨了OCTREE中的层次依赖性，以将每个非叶子Octree节点的占用信息编码为bitstream。此外，我们在本地拟合的二次表面，具有基于体素的几何感知模块，可在熵编码中提供几何先验。这些强大的先验使我们的熵框架能够将Octree编码为更紧凑的Bitstream。在解码阶段，我们采用了两步的启发式策略来以更好的重建质量恢复点云。定量评估表明，我们的方法的表现优于最先进的基准，比kitti Odometry和Nuscenes数据集的比特率提高了11-16％和12-14％。
We present a novel octree-based multi-level framework for large-scale point cloud compression, which can organize sparse and unstructured point clouds in a memory-efficient way. In this framework, we propose a new entropy model that explores the hierarchical dependency in an octree using the context of siblings' children, ancestors, and neighbors to encode the occupancy information of each non-leaf octree node into a bitstream. Moreover, we locally fit quadratic surfaces with a voxel-based geometry-aware module to provide geometric priors in entropy encoding. These strong priors empower our entropy framework to encode the octree into a more compact bitstream. In the decoding stage, we apply a two-step heuristic strategy to restore point clouds with better reconstruction quality. The quantitative evaluation shows that our method outperforms state-of-the-art baselines with a bitrate improvement of 11-16% and 12-14% on the KITTI Odometry and nuScenes datasets, respectively.
