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We present a pipeline for parametric wireframe extraction from denselysampled point clouds. Our approach processes a scalar distance field thatrepresents proximity to the nearest sharp feature curve. In intermediatestages, it detects corners, constructs curve segmentation, and builds atopological graph fitted to the wireframe. As an output, we produce parametricspline curves that can be edited and sampled arbitrarily. We evaluate ourmethod on 50 complex 3D shapes and compare it to the novel deep learning-basedtechnique, demonstrating superior quality.

3D Parametric Wireframe Extraction Based on Distance Fields