iPUNet：迭代交叉场引导的点云上采样
iPUNet:Iterative Cross Field Guided Point Cloud Upsampling
AI总结：
针对稀疏、噪声、非均匀点云，提出iPUNet，利用自监督交叉场引导局部参数化曲面实现任意比例上采样，并通过迭代细化分布，优于现有方法。
摘要：
由3D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。为了促进点云在下游应用中的可用性，针对此类输入，我们提出了一种基于学习的点云上采样方法，即iPUNet，该方法能够以任意比例生成密集且均匀的点，并更好地捕捉尖锐特征。为了生成特征感知的点，我们引入了通过自监督与尖锐几何特征对齐的交叉场来引导点生成。给定交叉场定义的坐标系，我们通过在每个输入点学习一个局部参数化曲面来实现任意比例的上采样。学习到的曲面将邻近点和2D切平面坐标作为输入，并映射到3D中的连续曲面，从而可以采样任意比例的输出点。为了解决输入点的非均匀性，在交叉场引导上采样的基础上，我们进一步引入了一种迭代策略，该策略在每次迭代中通过将稀疏点移动到期望的连续3D曲面来细化点分布。仅需少量迭代，稀疏点即可均匀分布，其对应的密集样本更加均匀且能更好地捕捉几何特征。通过对各种物体和场景扫描的广泛评估，我们证明了iPUNet能够稳健地处理噪声和非均匀分布的输入，并优于最先进的点云上采样方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
