SpUDD defines superpower contours from power diagrams of unsigned distance samples, proves convergence to the true surface, and uses them to generate approximating polygonal meshes that outperform prior strategies.
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Introduces overhauled level set construction, root-finding procedures, and regularized objective to compute Dirichlet eigenvalue minimizers that match or exceed prior best known results.
citing papers explorer
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SpUDD: Superpower Contouring of Unsigned Distance Data
SpUDD defines superpower contours from power diagrams of unsigned distance samples, proves convergence to the true surface, and uses them to generate approximating polygonal meshes that outperform prior strategies.
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A New Level Set Formulation for Improved Dirichlet Eigenvalue Minimizers
Introduces overhauled level set construction, root-finding procedures, and regularized objective to compute Dirichlet eigenvalue minimizers that match or exceed prior best known results.