Abstract
Reconstructing 3D point clouds into triangle meshes is a key problem in computational geometry and surface reconstruction. Point cloud triangulation solves this problem by providing edge information to the input points. Since no vertex interpolation is involved, it is beneficial to preserve sharp details on the surface. Taking advantage of learning-based techniques in triangulation, existing methods enumerate the complete combinations of candidate triangles, which is both complex and inefficient. In this paper, we leverage the duality between a triangle and its circumcenter, and introduce a deep neural network that detects the circumcenters to achieve point cloud triangulation. Specifically, we introduce multiple anchor priors to divide the neighborhood space of each point. The neural network then learns to predict the presences and locations of circumcenters under the guidance of those anchors. We extract the triangles dual to the detected circumcenters to form a primitive mesh, from which an edge-manifold mesh is produced via simple post-processing. Unlike existing learning-based triangulation methods, the proposed method bypasses an exhaustive enumeration of triangle combinations and local surface parameterization. We validate the efficiency, generalization, and robustness of our method on prominent datasets of both watertight and open surfaces. The code and trained models are provided at this https URL.
Abstract (translated)
将三维点云重构为三角形网格是计算几何和表面重建的关键问题。点云三角化通过向输入点提供边信息解决了这个问题。由于没有顶点插值,因此有益的是保留表面的锐利细节。利用三角化中的学习技术,现有方法列举了所有可能的组合,这是一个既复杂又效率不高的问题。在本文中,我们利用三角形及其周长的双端关系,并引入一个深度神经网络来检测周长以实现点云三角化。具体来说,我们引入了多个基准点以分割每个点周围的空间。神经网络随后学习在那些基准点的指导下预测周长的存在和位置。我们提取检测到周长的三角形作为基本网格,并通过简单的后处理生产边缘分支网格。与现有的基于学习的方法不同,我们提出的方法跳过了遍历三角形组合和局部表面参数化的任务。我们在不同的防水和开放表面上的重要数据集上验证我们的方法的效率、泛化性和鲁棒性。代码和训练模型在这个 https URL 上提供。
URL
https://arxiv.org/abs/2301.09253
