We survey and benchmark traditional and novel learning-based algorithms that address the problem of surface reconstruction from point clouds. Surface reconstruction from point clouds is particularly challenging when applied to real-world acquisitions, due to noise, outliers, non-uniform sampling and missing data. Traditionally, different handcrafted priors of the input points or the output surface have been proposed to make the problem more tractable. However, hyperparameter tuning for adjusting priors to different acquisition defects can be a tedious task. To this end, the deep learning community has recently addressed the surface reconstruction problem. In contrast to traditional approaches, deep surface reconstruction methods can learn priors directly from a training set of point clouds and corresponding true surfaces. In our survey, we detail how different handcrafted and learned priors affect the robustness of methods to defect-laden input and their capability to generate geometric and topologically accurate reconstructions. In our benchmark, we evaluate the reconstructions of several traditional and learning-based methods on the same grounds. We show that learning-based methods can generalize to unseen shape categories, but their training and test sets must share the same point cloud characteristics. We also provide the code and data to compete in our benchmark and to further stimulate the development of learning-based surface reconstruction https://github.com/raphaelsulzer/dsr-benchmark.

翻译：本文对从点云重建表面的传统算法与新兴学习算法进行了综述与基准测试分析。由于真实场景采集数据存在噪声、离群点、非均匀采样及数据缺失等问题，点云表面重建极具挑战性。传统方法通过设计输入点云或输出表面的手工先验知识来简化问题，但针对不同采集缺陷调整先验的超参数往往繁琐耗时。为此，深度学习领域近年来开始探索表面重建问题。与传统方法不同，深度表面重建方法可直接从包含点云及其对应真实表面的训练集中学习先验知识。本综述详细阐述了不同手工先验与学习先验如何影响算法对缺陷输入的鲁棒性，以及生成几何与拓扑精确重建的能力。在基准测试中，我们在统一条件下评估了多种传统与学习方法的重建效果，结果表明学习方法能够泛化至未见形状类别，但其训练集与测试集须具有相同的点云特性。我们同时提供了参与本基准测试的代码与数据集（https://github.com/raphaelsulzer/dsr-benchmark），以进一步推动基于学习的表面重建研究发展。