The Geometry-based Point Cloud Compression (G-PCC) has been developed by the
Moving Picture Experts Group to compress point clouds. In its lossy mode, the
reconstructed point cloud by G-PCC often suffers from noticeable distortions
due to the na\"{i}ve geometry quantization (i.e., grid downsampling). This
paper proposes a hierarchical prior-based super resolution method for point
cloud geometry compression. The content-dependent hierarchical prior is
constructed at the encoder side, which enables coarse-to-fine super resolution
of the point cloud geometry at the decoder side. A more accurate prior
generally yields improved reconstruction performance, at the cost of increased
bits required to encode this side information. With a proper balance between
prior accuracy and bit consumption, the proposed method demonstrates
substantial Bjontegaard-delta bitrate savings on the MPEG Cat1A dataset,
surpassing the octree-based and trisoup-based G-PCC v14. We provide our
implementations for reproducible research at
this https URL

该研究论文提出了一种基于几何的超分辨率方法，用于点云几何压缩，并通过构建内容相关的分层先验实现了粗粒度到细粒度的点云几何超分辨率，以在编码器和解码器之间实现更准确的先验，以减少位数消耗并获得资源占用等方面的性能改进。

基于分级优先的点云几何压缩的超分辨率

Hierarchical Prior-based Super Resolution for Point Cloud Geometry  Compression

This article introduces three-filters-to-normal+ (3F2N+), an extension of our
previous work three-filters-to-normal (3F2N), with a specific focus on
incorporating discontinuity discrimination capability into surface normal
estimators (SNEs). 3F2N+ achieves this capability by utilizing a novel
discontinuity discrimination module (DDM), which combines depth curvature
minimization and correlation coefficient maximization through conditional
random fields (CRFs). To evaluate the robustness of SNEs on noisy data, we
create a large-scale synthetic surface normal (SSN) dataset containing 20
scenarios (ten indoor scenarios and ten outdoor scenarios with and without
random Gaussian noise added to depth images). Extensive experiments demonstrate
that 3F2N+ achieves greater performance than all other geometry-based surface
normal estimators, with average angular errors of 7.85$^\circ$, 8.95$^\circ$,
9.25$^\circ$, and 11.98$^\circ$ on the clean-indoor, clean-outdoor,
noisy-indoor, and noisy-outdoor datasets, respectively. We conduct three
additional experiments to demonstrate the effectiveness of incorporating our
proposed 3F2N+ into downstream robot perception tasks, including freespace
detection, 6D object pose estimation, and point cloud completion. Our source
code and datasets are publicly available at this https URL

本研究介绍了一种名为 3F2N + 的新方法，它通过引入一个新的不连续性判别模块（DDM），结合深度曲率最小化和相关系数最大化以及条件随机场，将不连续性判别能力引入表面法线估计器。实验结果表明，3F2N + 在各种数据集上实现了优于其他基于几何的表面法线估计器的性能。此外，本研究还展示了将 3F2N + 应用于机器人感知任务中的有效性。