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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Unsupervised Shape representations for 3D reconstruction |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Non-uniform rational B-Spline surfaces (NURBS surface), a kind of parametric surface, are widely used in 3D modeling. This work explores NURBS surface reconstruction via the NURBS-Diff module. The NURBS-Diff module enables NURBS surfaces differentiable using the PyTorch framework. With supervised parameters, the module reconstructs the NURBS-based point cloud efficiently. This work introduces several pipelines by utilizing the NURBS-Diff module in unsupervised cases. The unsupervised pipelines make use of supersampling methods to obtain unstructured input and propose various metrics for point cloud and surface evaluation. The baseline unsupervised method is adapted from the original supervised pipeline. An extension of the NURBS-Diff module is also presented. The unsupervised pipelines are evaluated against the baseline. The pipelines serve as a stepping stone to further investigation into NURBS surface reconstruction based on unstructured input. |
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