ABC: A Big CAD Model Dataset For Geometric Deep Learning
We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation...
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| Veröffentlicht in: | arXiv.org 2019-04 |
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| creator | Koch, Sebastian Matveev, Albert Jiang, Zhongshi Williams, Francis Artemov, Alexey Burnaev, Evgeny Alexa, Marc Zorin, Denis Panozzo, Daniele |
| description | We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms. As a use case for our dataset, we perform a large-scale benchmark for estimation of surface normals, comparing existing data driven methods and evaluating their performance against both the ground truth and traditional normal estimation methods. |
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| identifier | EISSN: 2331-8422 |
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| subjects | CAD Collection Computer aided design Datasets Deep learning Differential geometry Ground truth Segmentation |
| title | ABC: A Big CAD Model Dataset For Geometric Deep Learning |
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