DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

• URL: http://arxiv.org/abs/2212.07766v3
• Date: Tue, 28 Mar 2023 13:59:47 GMT
• Title: DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients
• Authors: R\'emi Pautrat, Daniel Barath, Viktor Larsson, Martin R. Oswald, Marc Pollefeys
• Abstract summary: Line segments are increasingly used in vision tasks. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector.
• Score: 105.25109274550607
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. Their learned counterparts are more repeatable and can handle challenging images, but at the cost of a lower accuracy and a bias towards wireframe lines. We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector that can be trained in the wild without ground truth lines. Our new line segment detector, DeepLSD, processes images with a deep network to generate a line attraction field, before converting it to a surrogate image gradient magnitude and angle, which is then fed to any existing handcrafted line detector. Additionally, we propose a new optimization tool to refine line segments based on the attraction field and vanishing points. This refinement improves the accuracy of current deep detectors by a large margin. We demonstrate the performance of our method on low-level line detection metrics, as well as on several downstream tasks using multiple challenging datasets. The source code and models are available at https://github.com/cvg/DeepLSD.

Related papers

• Learning to Make Keypoints Sub-Pixel Accurate [80.55676599677824]
  This work addresses the challenge of sub-pixel accuracy in detecting 2D local features. We propose a novel network that enhances any detector with sub-pixel precision by learning an offset vector for detected features.
  arXiv  Detail & Related papers  (2024-07-16T12:39:56Z)
• TGGLinesPlus: A robust topological graph-guided computer vision algorithm for line detection from images [17.786188818725783]
  We propose and implement a topological graph-guided algorithm, named TGGLinesPlus, for line detection. Our experiments on images from a wide range of domains have demonstrated the flexibility of our algorithm. We hope our open-source implementation of TGGLinesPlus will inspire and pave the way for many applications where spatial science matters.
  arXiv  Detail & Related papers  (2024-03-26T18:49:56Z)
• Converting Depth Images and Point Clouds for Feature-based Pose Estimation [0.0]
  This paper presents a method of converting depth data into images capable of visualizing spatial details that are basically hidden in traditional depth images. Compared to Bearing Angle images, our method yields brighter, higher-contrast images with more visible contours and more details.
  arXiv  Detail & Related papers  (2023-10-23T13:29:42Z)
• Level-line Guided Edge Drawing for Robust Line Segment Detection [38.21854942764346]
  This paper proposes a level-line guided edge drawing for robust line segment detection (GEDRLSD) The level-line information provides potential directions for edge tracking, which could be served as a guideline for accurate edge drawing. Numerical experiments show the superiority of the proposed GEDRLSD algorithm compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-10T04:03:59Z)
• Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method [51.30748775681917]
  We consider the task of low-light image enhancement (LLIE) and introduce a large-scale database consisting of images at 4K and 8K resolution. We conduct systematic benchmarking studies and provide a comparison of current LLIE algorithms. As a second contribution, we introduce LLFormer, a transformer-based low-light enhancement method.
  arXiv  Detail & Related papers  (2022-12-22T09:05:07Z)
• SOLD2: Self-supervised Occlusion-aware Line Description and Detection [95.8719432775724]
  We introduce the first joint detection and description of line segments in a single deep network. Our method does not require any annotated line labels and can therefore generalize to any dataset. We evaluate our approach against previous line detection and description methods on several multi-view datasets.
  arXiv  Detail & Related papers  (2021-04-07T19:27:17Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)
• ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras [17.943949895764938]
  Line segment detection is essential for high-level tasks in computer vision and robotics. Currently, most stateof-the-art (SOTA) methods are dedicated to detecting straight line segments in undistorted pinhole images. We propose to target at the unified line segment detection (ULSD) for both distorted and undistorted images.
  arXiv  Detail & Related papers  (2020-11-06T03:30:17Z)
• Deep Hough Transform for Semantic Line Detection [70.28969017874587]
  We focus on a fundamental task of detecting meaningful line structures, a.k.a. semantic lines, in natural scenes. Previous methods neglect the inherent characteristics of lines, leading to sub-optimal performance. We propose a one-shot end-to-end learning framework for line detection.
  arXiv  Detail & Related papers  (2020-03-10T13:08:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.