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

TL;DR

This paper introduces Circle-Net, an end-to-end deep learning framework that combines boundary detection and algebraic fitting to accurately extract circles from raw point clouds, even in noisy conditions.

Contribution

The paper presents a novel co-trained network that integrates boundary point detection with weighted algebraic fitting, improving robustness and accuracy over existing methods.

Findings

Outperforms state-of-the-art methods in noise robustness

Achieves higher accuracy in circle extraction from real-scanned data

Demonstrates effectiveness on both synthetic and real datasets

Abstract

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.