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CGAL 6.2 - Feature graph
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The feature graph is extracted in multiple steps. First, a sharpness evaluation is conducted on the surface elements. Then, from this measure, smooth elements are discarded, and a graph thinning process generates lines that are guaranteed to be positioned along the highest values of the measure. Finally, the result is refined with a correction step and an optimization phase to ensure the correct placement of lines and corners along estimated normals.
Concepts representing the input surfaces:
Secondary concepts:
CGAL::Feature_graph::Detect_sharp_features_on_labeled_image()CGAL::Feature_graph::Detect_sharp_features_on_surface()CGAL::Feature_graph::Regularization_parameters_on_imageCGAL::Feature_graph::Regularization_parameters_on_surfaceCGAL::Feature_graph::Optimization_parameters_on_imageCGAL::Feature_graph::Optimization_parameters_on_surfaceCGAL::Feature_graph::AmbrosioTortorelli_on_image::Sharpness_functorCGAL::Feature_graph::Sharpness_estimator::Sharpness_estimator_on_surfaceCGAL::Feature_graph::AmbrosioTortorelli_on_image::Normal_functorCGAL::Feature_graph::Normal_estimator::Normal_estimator_on_surface Modules | |
| Concepts | |
| Feature Graph Detector | |
| The functors in this group allows to extract the feature graph from different types of surfaces. | |
| Sharpness Estimator | |
| The functors in this group allows to estimate the sharpness on surfaces. | |
| Normal Estimator | |
| The functors in this group allows to estimate the normal on surfaces. | |
| Parameter Class | |
| The classes in this group provide various parameters for the feature detection. | |
Classes | |
| struct | CGAL::Feature_graph::AmbrosioTortorelli_on_image< Vector_3 > |
| Class that evaluates the Ambriosio-Tortorelli normals and sharpness measure from an image. More... | |