This study develops a unified Point Cloud Geometry (PCG) compression method through Sparse Tensor Processing (STP) based multiscale representation of voxelized PCG, dubbed as the SparsePCGC. Applying the STP reduces the complexity significantly because it only performs the convolutions centered at Most-Probable Positively-Occupied Voxels (MP-POV). And the multiscale representation facilitates us to compress scale-wise MP-POVs progressively. The overall compression efficiency highly depends on the approximation accuracy of occupancy probability of each MP-POV. Thus, we design the Sparse Convolution based Neural Networks (SparseCNN) consisting of sparse convolutions and voxel re-sampling to extensively exploit priors. We then develop the SparseCNN based Occupancy Probability Approximation (SOPA) model to estimate the occupancy probability in a single-stage manner only using the cross-scale prior or in multi-stage by step-wisely utilizing autoregressive neighbors. Besides, we also suggest the SparseCNN based Local Neighborhood Embedding (SLNE) to characterize the local spatial variations as the feature attribute to improve the SOPA. Our unified approach shows the state-of-art performance in both lossless and lossy compression modes across a variety of datasets including the dense PCGs (8iVFB, Owlii) and the sparse LiDAR PCGs (KITTI, Ford) when compared with the MPEG G-PCC and other popular learning-based compression schemes. Furthermore, the proposed method presents lightweight complexity due to point-wise computation, and tiny storage desire because of model sharing across all scales. We make all materials publicly accessible at https://github.com/NJUVISION/SparsePCGC for reproducible research.

本研究提出了一种基于多尺度稀疏张量的点云几何压缩方法，称为SparsePCGC，使用Sparse Convolution-based Neural Network和Occupancy Probability Approximation模型计算不同尺度的空间相关性来提高估计空间占用概率的准确性和压缩效率。在各种数据集中，该方法在无损和有损压缩模式下均表现出与标准化的MPEG G-PCC以及流行的基于学习的方案相比的最先进性能，同时具有低复杂度的优势。

基于稀疏张量的点云几何多尺度表示压缩