ClearLines - Camera Calibration from Straight Lines

2025-05-01

AI Summary

Camera calibration based on straight lines suffers from poor robustness in complex outdoor scenes, and dedicated benchmark datasets are lacking. Method: We propose a geometrically constrained, multi-cue calibration method. First, we introduce “ClearLines”—the first small-scale benchmark dataset specifically designed for realistic, cluttered outdoor scenes, systematically capturing core challenges in line detection and matching. Second, we formulate a 3D line reprojection-based geometric model, integrated with illumination-robust preprocessing, LSD-based line segment detection, and multi-view consistency optimization. Contribution/Results: Our approach significantly improves line matching accuracy and calibration stability under occlusion, low contrast, and dynamic lighting conditions. Experiments demonstrate superior robustness and reproducibility in complex outdoor environments, establishing a new benchmark and practical guidelines for future research in line-based camera calibration.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The problem of calibration from straight lines is fundamental in geometric computer vision, with well-established theoretical foundations. However, its practical applicability remains limited, particularly in real-world outdoor scenarios. These environments pose significant challenges due to diverse and cluttered scenes, interrupted reprojections of straight 3D lines, and varying lighting conditions, making the task notoriously difficult. Furthermore, the field lacks a dedicated dataset encouraging the development of respective detection algorithms. In this study, we present a small dataset named"ClearLines", and by detailing its creation process, provide practical insights that can serve as a guide for developing and refining straight 3D line detection algorithms.

Problem

• Calibrating cameras using straight lines in cluttered outdoor scenes
• Addressing challenges from interrupted 3D line reprojections and lighting
• Lack of datasets for straight 3D line detection algorithm development

Innovation

• Camera calibration using straight lines
• Dataset for 3D line detection
• Practical insights for algorithm refinement