Recently, arbitrary-scale point cloud upsampling mechanism became
increasingly popular due to its efficiency and convenience for practical
applications. To achieve this, most previous approaches formulate it as a
problem of surface approximation and employ point-based networks to learn
surface representations. However, learning surfaces from sparse point clouds is
more challenging, and thus they often suffer from the low-fidelity geometry
approximation. To address it, we propose an arbitrary-scale Point cloud
Upsampling framework using Voxel-based Network (\textbf{PU-VoxelNet}). Thanks
to the completeness and regularity inherited from the voxel representation,
voxel-based networks are capable of providing predefined grid space to
approximate 3D surface, and an arbitrary number of points can be reconstructed
according to the predicted density distribution within each grid cell. However,
we investigate the inaccurate grid sampling caused by imprecise density
predictions. To address this issue, a density-guided grid resampling method is
developed to generate high-fidelity points while effectively avoiding sampling
outliers. Further, to improve the fine-grained details, we present an auxiliary
training supervision to enforce the latent geometric consistency among local
surface patches. Extensive experiments indicate the proposed approach
outperforms the state-of-the-art approaches not only in terms of fixed
upsampling rates but also for arbitrary-scale upsampling.

最近，基于点云的任意尺度上采样机制因其在实际应用中的效率和便利性而越来越受欢迎。为了解决从稀疏点云学习表面表示所面临的挑战，我们提出了一种使用基于体素网络的任意尺度点云上采样框架（PU-VoxelNet）。通过利用体素表达的完整性和规律性，体素网络能够提供预定义的网格空间来近似三维表面，并根据每个网格单元内的预测密度分布重建任意数量的点。进一步，为了改善细节，我们提供了一种辅助训练监督方法，以强制实现局部表面块之间的潜在几何一致性。广泛的实验证明，所提出的方法在固定上采样率和任意尺度上采样方面都优于现有技术。

基于体素网络的任意尺度点云上采样与隐式几何一致性学习

Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with  Latent Geometric-Consistent Learning

Although point-based networks are demonstrated to be accurate for 3D point
cloud modeling, they are still falling behind their voxel-based competitors in
3D detection. We observe that the prevailing set abstraction design for
down-sampling points may maintain too much unimportant background information
that can affect feature learning for detecting objects. To tackle this issue,
we propose a novel set abstraction method named Semantics-Augmented Set
Abstraction (SASA). Technically, we first add a binary segmentation module as
the side output to help identify foreground points. Based on the estimated
point-wise foreground scores, we then propose a semantics-guided point sampling
algorithm to help retain more important foreground points during down-sampling.
In practice, SASA shows to be effective in identifying valuable points related
to foreground objects and improving feature learning for point-based 3D
detection. Additionally, it is an easy-to-plug-in module and able to boost
various point-based detectors, including single-stage and two-stage ones.
Extensive experiments on the popular KITTI and nuScenes datasets validate the
superiority of SASA, lifting point-based detection models to reach comparable
performance to state-of-the-art voxel-based methods.

本论文提出了一种名为 SASA 的新型集抽象方法，通过添加二进制分割模块和基于预测的前景分数的语义引导点采样算法来识别与前景物体相关的有价值的点，进而提升点云三维检测的性能。实验表明，SASA 可提升各种基于点云的检测器的性能，与体素方法媲美。