This study develops a unified Point Cloud Geometry (PCG) compression method
through the processing of multiscale sparse tensor-based voxelized PCG. We call
this compression method SparsePCGC. The proposed SparsePCGC is a low complexity
solution because it only performs the convolutions on sparsely-distributed
Most-Probable Positively-Occupied Voxels (MP-POV). The multiscale
representation also allows us to compress scale-wise MP-POVs by exploiting
cross-scale and same-scale correlations extensively and flexibly. The overall
compression efficiency highly depends on the accuracy of estimated occupancy
probability for each MP-POV. Thus, we first design the Sparse Convolution-based
Neural Network (SparseCNN) which stacks sparse convolutions and voxel sampling
to best characterize and embed spatial correlations. We then develop the
SparseCNN-based Occupancy Probability Approximation (SOPA) model to estimate
the occupancy probability either in a single-stage manner only using the
cross-scale correlation, or in a multi-stage manner by exploiting stage-wise
correlation among same-scale neighbors. Besides, we also suggest the SparseCNN
based Local Neighborhood Embedding (SLNE) to aggregate local variations as
spatial priors in feature attribute to improve the SOPA. Our unified approach
not only shows state-of-the-art performance in both lossless and lossy
compression modes across a variety of datasets including the dense object PCGs
(8iVFB, Owlii, MUVB) and sparse LiDAR PCGs (KITTI, Ford) when compared with
standardized MPEG G-PCC and other prevalent learning-based schemes, but also
has low complexity which is attractive to practical applications.

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。