CircNet：通过外心检测对3D点云进行网格化
CircNet: Meshing 3D Point Clouds with Circumcenter Detection
AI总结：
针对现有基于学习的点云三角剖分方法枚举三角形组合效率低的问题，提出CircNet，通过检测三角形外心实现三角剖分，在水密和开放表面数据集上验证了效率、泛化性与鲁棒性。
摘要：
将3D点云重建为三角形网格是计算几何和表面重建领域的核心问题。点云三角剖分通过为输入点提供边信息来解决该问题，由于不涉及顶点插值，该方法有利于保留表面的尖锐细节。现有方法借助基于学习的三角剖分技术，会枚举候选三角形的所有组合，过程既复杂又低效。本文利用三角形与其外心之间的对偶性，提出了一种通过检测外心实现点云三角剖分的深度神经网络。具体而言，我们引入多个锚点先验来划分每个点的邻域空间，神经网络随后在这些锚点的引导下学习预测外心的存在性与位置。我们提取与检测到的外心对偶的三角形，形成初始网格，再通过简单的后处理得到边流形网格。与现有基于学习的三角剖分方法不同，所提方法无需穷举三角形组合，也无需进行局部表面参数化。我们在水密表面和开放表面的主流数据集上验证了该方法的效率、泛化性与鲁棒性。代码与训练好的模型已发布于https://github.com/EnyaHermite/CircNet。
英文摘要：
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 https://github.com/EnyaHermite/CircNet.
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。
