A bio-inspired approach to line segment detection utilizing orientation-selective neurons

Abstract

Line segment detection is essential for tasks like SLAM, camera pose estimation, and 3D reconstruction. Although many excellent line segment detection methods have been proposed, detecting more true positives while rejecting false positives remains challenging. The human visual system can effectively perceive line segments in complex environments through a processing pathway involving multiple visual cortices. Inspired by this, we propose a novel bio-inspired line segment detection method that mimics the perception of line segments in the visual cortex. Our method models orientation-selective neurons in the primary and secondary visual cortices. Based on the preferred orientations of these neurons, we integrate them to mimic the function of orientation and curvature domains in the fourth visual cortex, generating continuous and smooth edge segments. A post-processing step, including least squares line fitting and gap merging, is employed to obtain line segments. We evaluated our method against other state-of-the-art methods on YorkUrban-LineSegment and Wireframe. Results show that our method achieves a higher F-score, improving by 2.9% and 2.1%, respectively, while ensuring both precision and recall. Additionally, in 3D reconstruction, our method produces more complete and accurate scenes with fewer fragments and omissions compared to other methods. Our code is available at https://github.com/DaipengYang7/BILSD.