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.
翻译:本研究通过处理多尺度的分散式高压软化PCG, 开发了统一的点云测量法(PCG)压缩方法。 我们称之为压缩法 SprasserPCGC。 拟议的SprasserPCGC 是一个低复杂性的解决方案, 因为它只是对分散的分散式高分解体( MP- POV) 进行演化。 多尺度的演示还使我们能够通过广泛和灵活地利用跨尺度和同一尺度的对应关系来压缩规模化的MP- POV。 总体压缩效率高度取决于每个MPC- POV的估计占用概率的准确性。 因此, 我们首先设计基于松散的混杂变异型神经网络(SparseCNN), 将稀释式的卷变和 voxel 样本堆积起来, 也就是: 混凝固的内压变异型( SprassyCN NPC- dable Appic) 模式, 仅以单一阶段的方式估算占用概率, 仅使用跨尺度的、 低级的物体, 或者以低级的低级的内压式的内压法, 的内压式的内压式的内基变变变, 也显示我们级的内基的内基的内压的内基的内基的内基的内基的内基的内基的内基的内基的内基的内变变变。