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Reconstructing 3D point clouds into triangle meshes is a key problem incomputational geometry and surface reconstruction. Point cloud triangulationsolves this problem by providing edge information to the input points. Since novertex interpolation is involved, it is beneficial to preserve sharp details onthe 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 dualitybetween a triangle and its circumcenter, and introduce a deep neural networkthat detects the circumcenters to achieve point cloud triangulation.Specifically, we introduce multiple anchor priors to divide the neighborhoodspace of each point. The neural network then learns to predict the presencesand locations of circumcenters under the guidance of those anchors. We extractthe triangles dual to the detected circumcenters to form a primitive mesh, fromwhich an edge-manifold mesh is produced via simple post-processing. Unlikeexisting learning-based triangulation methods, the proposed method bypasses anexhaustive enumeration of triangle combinations and local surfaceparameterization. We validate the efficiency, generalization, and robustness ofour method on prominent datasets of both watertight and open surfaces. The codeand trained models are provided at https://github.com/EnyaHermite/CircNet.

CircNet: Meshing 3D Point Clouds with Circumcenter Detection