3D Parametric Wireframe Extraction Based on Distance Fields

Albert Matveev1Alexey Artemov1Denis Zorin2, 1Evgeny Burnaev1

1Skolkovo Institute of Science and Technology2New York University

The 4th International Conference on Artificial Intelligence and Pattern Recognition 2021

Parametric wireframe extraction pipeline. (a) -- dense point cloud with estimated distance field, (b) -- sharp point skeleton with color-coded segmentation into individual curves (note the black clusters corresponding to the detected corner neighborhoods), (c) -- optimized topological graph with final corner points (red), (d) -- extracted parametric wireframe, (e) -- ground-truth parametric wireframe.


We present a pipeline for parametric wireframe extraction from densely sampled point clouds. Our approach processes a scalar distance field that represents proximity to the nearest sharp feature curve. In intermediate stages, it detects corners, constructs curve segmentation, and builds a topological graph fitted to the wireframe. As an output, we produce parametric spline curves that can be edited and sampled arbitrarily. We evaluate our method on 50 complex 3D shapes and compare it to the novel deep learning-based technique, demonstrating superior quality.



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