Abstract
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 this https URL.
Abstract (translated)
我们综述并基准了传统的和新型基于学习的算法,以解决从点云表面重建的问题。在实际应用中,从点云表面重建的问题特别具有挑战性,因为噪声、异常值、非均匀采样和缺失数据。传统上,提出了不同手工计算的先验概率,以使问题更可处理。然而,调整不同获取缺陷的先验概率是一项繁琐的任务。为此,深度学习社区最近解决了表面重建问题。与传统方法不同,深度表面重建方法可以从训练集中的点云和对应的真实表面学习先验概率。在我们的调查中,我们详细阐述了不同手工计算和学习先验如何影响方法对缺陷负荷的鲁棒性和生成几何和拓扑准确的重构能力。在我们的基准中,我们评估了多种传统和基于学习的方法的重构性能,并在同一标准下进行比较。我们表明,基于学习的方法可以泛化到未曾见过的形状类别中,但其训练和测试集必须共享相同的点云特征。我们 also提供了代码和数据,以在我们的基准中竞争,并进一步刺激基于学习的表面重建技术的发展,该URL指向https。
URL
https://arxiv.org/abs/2301.13656
