PyFRep: Shape Modeling with Differentiable Function Representation
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909585631084544 |
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| author | Fayolle, Pierre-Alain Maltsev, Evgenii |
| author_facet | Fayolle, Pierre-Alain Maltsev, Evgenii |
| contents | We propose a framework for performing differentiable geometric modeling based on the Function Representation (FRep). The framework is built on top of modern libraries for performing automatic differentiation allowing us to obtain derivatives w.r.t. space or shape parameters. We demonstrate possible applications of this framework: Curvature estimation for shape interrogation, signed distance function computation and approximation and fitting shape parameters of a parametric model to data. Our framework is released as open-source. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14216 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PyFRep: Shape Modeling with Differentiable Function Representation Fayolle, Pierre-Alain Maltsev, Evgenii Graphics We propose a framework for performing differentiable geometric modeling based on the Function Representation (FRep). The framework is built on top of modern libraries for performing automatic differentiation allowing us to obtain derivatives w.r.t. space or shape parameters. We demonstrate possible applications of this framework: Curvature estimation for shape interrogation, signed distance function computation and approximation and fitting shape parameters of a parametric model to data. Our framework is released as open-source. |
| title | PyFRep: Shape Modeling with Differentiable Function Representation |
| topic | Graphics |
| url | https://arxiv.org/abs/2504.14216 |