  The shape of circle is one of fundamental geometric primitives of man-made
engineering objects. Thus, extraction of circles from scanned point clouds is a
quite important task in 3D geometry data processing. However, existing circle
extraction methods either are sensitive to the quality of raw point clouds when
classifying circle-boundary points, or require well-designed fitting functions
when regressing circle parameters. To relieve the challenges, we propose an
end-to-end Point Cloud Circle Algebraic Fitting Network (Circle-Net) based on a
synergy of deep circle-boundary point feature learning and weighted algebraic
fitting. First, we design a circle-boundary learning module, which considers
local and global neighboring contexts of each point, to detect all potential
circle-boundary points. Second, we develop a deep feature based circle
parameter learning module for weighted algebraic fitting, without designing any
weight metric, to avoid the influence of outliers during fitting. Unlike most
of the cutting-edge circle extraction wisdoms, the proposed
classification-and-fitting modules are originally co-trained with a
comprehensive loss to enhance the quality of extracted circles.Comparisons on
the established dataset and real-scanned point clouds exhibit clear
improvements of Circle-Net over SOTAs in terms of both noise-robustness and
extraction accuracy. We will release our code, model, and data for both
training and evaluation on GitHub upon publication.
