Surface reconstruction for point clouds is an important task in 3D computer vision. Most of the latest methods resolve this problem by learning signed distance functions from point clouds, which are limited to reconstructing closed surfaces. Some other methods tried to represent open surfaces using unsigned distance functions (UDF) which are learned from ground truth distances. However, the learned UDF is hard to provide smooth distance fields due to the discontinuous character of point clouds. In this paper, we propose CAP-UDF, a novel method to learn consistency-aware UDF from raw point clouds. We achieve this by learning to move queries onto the surface with a field consistency constraint, where we also enable to progressively estimate a more accurate surface. Specifically, we train a neural network to gradually infer the relationship between queries and the approximated surface by searching for the moving target of queries in a dynamic way. Meanwhile, we introduce a polygonization algorithm to extract surfaces using the gradients of the learned UDF. We conduct comprehensive experiments in surface reconstruction for point clouds, real scans or depth maps, and further explore our performance in unsupervised point normal estimation, which demonstrate non-trivial improvements of CAP-UDF over the state-of-the-art methods.

翻译：点云表面重建是三维计算机视觉中的重要任务。大多数最新方法通过学习点云的有符号距离函数来解决该问题，但这类方法仅限于重建封闭表面。其他一些方法尝试使用无符号距离函数（UDF）表示开放表面，这些函数通过真实距离数据学习得到。然而，由于点云数据的不连续性，学习得到的UDF难以提供平滑的距离场。本文提出CAP-UDF——一种从原始点云学习一致性感知UDF的新方法。我们通过场一致性约束学习将查询点移动至表面，并在此过程中逐步估计更精确的表面。具体而言，我们训练神经网络以动态方式搜索查询点的移动目标，从而渐进推断查询点与近似表面之间的关系。同时，我们引入一种多边形化算法，利用学习所得UDF的梯度进行表面提取。我们在点云表面重建、真实扫描数据及深度图重建任务中进行了全面实验，并进一步探索了方法在无监督点云法向估计中的性能，实验结果表明CAP-UDF相较现有最优方法取得了显著提升。