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The shape of circle is one of fundamental geometric primitives of man-madeengineering objects. Thus, extraction of circles from scanned point clouds is aquite important task in 3D geometry data processing. However, existing circleextraction methods either are sensitive to the quality of raw point clouds whenclassifying circle-boundary points, or require well-designed fitting functionswhen regressing circle parameters. To relieve the challenges, we propose anend-to-end Point Cloud Circle Algebraic Fitting Network (Circle-Net) based on asynergy of deep circle-boundary point feature learning and weighted algebraicfitting. First, we design a circle-boundary learning module, which considerslocal and global neighboring contexts of each point, to detect all potentialcircle-boundary points. Second, we develop a deep feature based circleparameter learning module for weighted algebraic fitting, without designing anyweight metric, to avoid the influence of outliers during fitting. Unlike mostof the cutting-edge circle extraction wisdoms, the proposedclassification-and-fitting modules are originally co-trained with acomprehensive loss to enhance the quality of extracted circles.Comparisons onthe established dataset and real-scanned point clouds exhibit clearimprovements of Circle-Net over SOTAs in terms of both noise-robustness andextraction accuracy. We will release our code, model, and data for bothtraining and evaluation on GitHub upon publication.

Deep Algebraic Fitting for Multiple Circle Primitives Extraction from Raw Point Clouds