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

Normal estimation for 3D point clouds is a fundamental task in 3D geometry
processing. The state-of-the-art methods rely on priors of fitting local
surfaces learned from normal supervision. However, normal supervision in
benchmarks comes from synthetic shapes and is usually not available from real
scans, thereby limiting the learned priors of these methods. In addition,
normal orientation consistency across shapes remains difficult to achieve
without a separate post-processing procedure. To resolve these issues, we
propose a novel method for estimating oriented normals directly from point
clouds without using ground truth normals as supervision. We achieve this by
introducing a new paradigm for learning neural gradient functions, which
encourages the neural network to fit the input point clouds and yield unit-norm
gradients at the points. Specifically, we introduce loss functions to
facilitate query points to iteratively reach the moving targets and aggregate
onto the approximated surface, thereby learning a global surface representation
of the data. Meanwhile, we incorporate gradients into the surface approximation
to measure the minimum signed deviation of queries, resulting in a consistent
gradient field associated with the surface. These techniques lead to our deep
unsupervised oriented normal estimator that is robust to noise, outliers and
density variations. Our excellent results on widely used benchmarks demonstrate
that our method can learn more accurate normals for both unoriented and
oriented normal estimation tasks than the latest methods. The source code and
pre-trained model are publicly available at this https URL

通过引入一种新的神经梯度函数学习范式，我们提出了一种从点云中直接估计定向法线的新方法，它能够更准确地学习未定向和定向法线估计任务。