Point clouds acquired by 3D scanning devices are often sparse, noisy, and
non-uniform, causing a loss of geometric features. To facilitate the usability
of point clouds in downstream applications, given such input, we present a
learning-based point upsampling method, i.e., iPUNet, which generates dense and
uniform points at arbitrary ratios and better captures sharp features. To
generate feature-aware points, we introduce cross fields that are aligned to
sharp geometric features by self-supervision to guide point generation. Given
cross field defined frames, we enable arbitrary ratio upsampling by learning at
each input point a local parameterized surface. The learned surface consumes
the neighboring points and 2D tangent plane coordinates as input, and maps onto
a continuous surface in 3D where arbitrary ratios of output points can be
sampled. To solve the non-uniformity of input points, on top of the cross field
guided upsampling, we further introduce an iterative strategy that refines the
point distribution by moving sparse points onto the desired continuous 3D
surface in each iteration. Within only a few iterations, the sparse points are
evenly distributed and their corresponding dense samples are more uniform and
better capture geometric features. Through extensive evaluations on diverse
scans of objects and scenes, we demonstrate that iPUNet is robust to handle
noisy and non-uniformly distributed inputs, and outperforms state-of-the-art
point cloud upsampling methods.

通过学习的点云上采样方法 iPUNet，可以在任意比例下生成密集、均匀的点，更好地捕捉尖锐特征；通过引入与尖锐几何特征对齐的横向场，使得生成的点在特征感知方面更优秀；同时，在横向场引导的上采样基础上，通过迭代策略将稀疏点移动到期望的连续三维表面上，使得稀疏点均匀分布且对应的密集样本更加均匀，更加准确地捕捉几何特征。经过广泛的对象和场景扫描评估，证明 iPUNet 能够处理嘈杂和非均匀分布的输入，优于现有的点云上采样方法。