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.

翻译：三维扫描设备获取的点云通常稀疏、噪声大且分布不均匀，导致几何特征丢失。为提升点云在下游应用中的可用性，针对此类输入，我们提出一种基于学习的点云上采样方法——iPUNet，该方法能以任意倍数生成密集均匀的点，并更好地捕捉尖锐特征。为生成特征感知的点，我们引入与尖锐几何特征对齐的交叉场，通过自监督方式引导点生成。基于交叉场定义的坐标系，我们通过在每个输入点处学习局部参数化曲面，实现任意倍数的上采样。该学习曲面以邻域点和二维切平面坐标为输入，映射至三维连续曲面，从而可从中采样任意数量的输出点。为解决输入点的不均匀性问题，我们在交叉场引导上采样的基础上进一步引入迭代策略：每次迭代中将稀疏点移动至目标连续三维曲面以优化点分布。仅需少量迭代，稀疏点即可均匀分布，其对应的密集采样点更均匀且能更好捕捉几何特征。通过对多种物体与场景扫描数据的广泛评估，我们证明iPUNet能稳健处理带噪声且分布不均的输入，其性能优于当前最先进的点云上采样方法。