Medial Axis Aware Learning of Signed Distance Functions
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917417607757824 |
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| author | Weidemaier, Samuel Norden-Smoch, Christoph Rumpf, Martin |
| author_facet | Weidemaier, Samuel Norden-Smoch, Christoph Rumpf, Martin |
| contents | We propose a novel variational method to compute a highly accurate global signed distance function (SDF) to a given point cloud. To this end, the jump set of the gradient of the SDF, which coincides with the medial axis of the surface, is explicitly taken into account through a higher-order variational formulation that enforces linear growth along the gradient direction away from this discontinuity set. The eikonal equation and the zero-level set of the SDF are enforced as constraints. To make this variational problem computationally tractable, a phase field approximation of Ambrosio-Tortorelli type is employed. The associated phase field function implicitly describes the medial axis. The method is implemented for surfaces represented by unoriented point clouds using neural network approximations of both the SDF and the phase field. Experiments demonstrate the method's accuracy both in the near field and globally. Quantitative and qualitative comparisons with other approaches show the advantages of the proposed method. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_16512 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Medial Axis Aware Learning of Signed Distance Functions Weidemaier, Samuel Norden-Smoch, Christoph Rumpf, Martin Computer Vision and Pattern Recognition Computational Geometry Graphics Machine Learning Numerical Analysis 65K10, 68T07, 65D18, 49J45 G.1.6; I.3.5; I.3.7; I.4.5 We propose a novel variational method to compute a highly accurate global signed distance function (SDF) to a given point cloud. To this end, the jump set of the gradient of the SDF, which coincides with the medial axis of the surface, is explicitly taken into account through a higher-order variational formulation that enforces linear growth along the gradient direction away from this discontinuity set. The eikonal equation and the zero-level set of the SDF are enforced as constraints. To make this variational problem computationally tractable, a phase field approximation of Ambrosio-Tortorelli type is employed. The associated phase field function implicitly describes the medial axis. The method is implemented for surfaces represented by unoriented point clouds using neural network approximations of both the SDF and the phase field. Experiments demonstrate the method's accuracy both in the near field and globally. Quantitative and qualitative comparisons with other approaches show the advantages of the proposed method. |
| title | Medial Axis Aware Learning of Signed Distance Functions |
| topic | Computer Vision and Pattern Recognition Computational Geometry Graphics Machine Learning Numerical Analysis 65K10, 68T07, 65D18, 49J45 G.1.6; I.3.5; I.3.7; I.4.5 |
| url | https://arxiv.org/abs/2604.16512 |