DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients (2212.07766v3)

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.

Overview of DeepLSD: Integrating Traditional and Learned Approaches for Line Segment Detection

The paper, "DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients," presents a novel approach to line segment detection by combining the strengths of traditional handcrafted methods and modern deep learning techniques. This integration aims to address the limitations inherent in both approaches when used independently.

The Problem Addressed

Line segments are critical features in various computer vision applications such as 3D reconstruction, SLAM, and visual localization. Traditional detectors based on image gradients, like LSD, offer speed and accuracy but struggle with robustness in noisy or low-illumination conditions. Conversely, learned detectors demonstrate improved repeatability in challenging settings but often at the cost of accuracy and are biased towards simpler wireframe lines.

Proposed Methodology

DeepLSD introduces a hybrid model that seeks to harness the precision of traditional methods and the robustness of deep learning:

1. Line Attraction Field: The authors propose a deep network to process images into a line attraction field, essentially a dual representation of line segments converted into image gradients suitable for deep learning.
2. Pseudo Ground Truth: Ground truth is not strictly required; instead, predictions are guided by bootstrapping existing line detectors using a method akin to homography adaptation. This generates a reliable distance and angle field from which the line segments can be extracted.
3. Surrogate Gradient: The output from the deep network serves as a surrogate for image gradients, feeding into traditional line detectors such as LSD. This enables the use of established heuristic-driven methods to determine line endpoints with high accuracy.
4. Refinement via Optimization: A novel optimization tool refines detected line segments using predicted fields and vanishing points, offering sizable improvements in accuracy.

Results and Implications

The method is evaluated using both low-level metrics and downstream tasks:

• Low-Level Metrics: On datasets like HPatches and RDNIM, DeepLSD balances repeatability and accuracy, outperforming standalone traditional and learned methods in homography estimation tasks.
• 3D Reconstruction: The hybrid detector improves both recall and precision of 3D line reconstructions compared to baselines such as LSD and SOLD2.
• Visual Localization: In challenging environments, notably the 7Scenes "Stairs" dataset, DeepLSD enhances pose estimation performance by leveraging line features in low-textured areas.
• Vanishing Point Estimation: The approach effectively estimates vanishing points, demonstrating its utility in structured scene understanding.

Future Directions

The paper opens avenues for further research in the integration of deep learning with classical techniques in various vision tasks beyond line detection. The model’s adaptability to various datasets without extensive labeled data heralds potential applications in other domains where feature extraction is paramount.

Conclusion

By coupling the robustness of deep learning with the precision of classical methods, DeepLSD presents a sophisticated, versatile solution to line segment detection, with significant practical implications for real-world applications in computer vision and beyond. This multidisciplinary approach may inspire further hybrid models that leverage the distinct strengths of independent methodologies.