ULSD: Unified line segment detection across pinhole, fisheye, and spherical cameras

Abstract

Image line segment detection is a fundamental problem in computer vision and remote sensing. Although numerous state-of-the-art methods have shown great performance for straight line segment detection, line segment detection for distorted images without undistortion is still a challenging problem. Besides, there is a lack of a unified line segment detection framework for both distorted and undistorted images. To address these two problems, we propose a novel learning-based Unified Line Segment Detection method (i.e., ULSD) for distorted and undistorted images in this paper. Specifically, we first propose a novel equipartition point-based Bezier curve representation to model arbitrary distorted line segments. Then the line segment detection is tackled by equipartition point regression with an end-to-end trainable neural network. Consequently, the proposed ULSD is independent of camera distortion parameters and does not need any undistortion preprocessing. In the experiments, the proposed method is firstly evaluated on the pinhole, fisheye, and spherical image datasets, respectively, as well as trained and tested on the mixed dataset with differently distorted images. The experimental results on each distortion model show that the proposed ULSD is more competitive than the state-of-the-art methods for both accuracy and efficiency, especially for the results of the unified model trained on the mixed datasets, thus demonstrating the effectiveness and generality of the proposed ULSD to real-world scenarios.