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ClearLines - Camera Calibration from Straight Lines

Gregory Schroeder, Mohamed Sabry, Cristina Olaverri-Monreal

TL;DR

The paper tackles the gap between theory and practice in camera calibration from straight lines in outdoor environments by introducing the ClearLines dataset, a compact collection derived from KITTI and IAMCV images. It provides a practical edge-segment detection pipeline, a high-recall labeling strategy, and an evaluation framework (precision and recall) tailored to straight-edge segments suitable for calibration. Key contributions include dataset creation and labeling workflow, an end-to-end pipeline outline (preprocessing, edge detection, chaining, subpixel refinement, merging, and filtering), and guidance on robust distortion estimation using the Brown-Conrady model and MSAC-based RANSAC. The work enables researchers to evaluate and refine straight-line calibration methods in realistic outdoor settings and highlights avenues for dataset expansion and integration with SLAM-based calibration.

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.

ClearLines - Camera Calibration from Straight Lines

TL;DR

The paper tackles the gap between theory and practice in camera calibration from straight lines in outdoor environments by introducing the ClearLines dataset, a compact collection derived from KITTI and IAMCV images. It provides a practical edge-segment detection pipeline, a high-recall labeling strategy, and an evaluation framework (precision and recall) tailored to straight-edge segments suitable for calibration. Key contributions include dataset creation and labeling workflow, an end-to-end pipeline outline (preprocessing, edge detection, chaining, subpixel refinement, merging, and filtering), and guidance on robust distortion estimation using the Brown-Conrady model and MSAC-based RANSAC. The work enables researchers to evaluate and refine straight-line calibration methods in realistic outdoor settings and highlights avenues for dataset expansion and integration with SLAM-based calibration.

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.
Paper Structure (16 sections, 3 equations, 4 figures, 2 tables)

This paper contains 16 sections, 3 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Definitions and Notation:
  3. Contributions:
  4. Paper Outline:
  5. Related Work
  6. ClearLines Dataset
  7. Evaluation:
  8. Pipeline Overview
  9. Image Preprocessing:
  10. Edge Detection:
  11. Edge Chaining:
  12. Subpixel refinement
  13. Merging:
  14. Filtering:
  15. Discussion & Future Work
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Examples from the "ClearLines" dataset. First column: Original images (from IAMCV dataset 2023-JKU-dataset and KITTI dataset 2013-kitti-dataset). Second column: output of our edge-segment detection pipeline. Third column: Results after manual filtering. All images in black-white for visualization purpose
  • Figure 2: Example output from the "ClearLines" evaluation framework. Green: True positives, Red: False positives, Orange: False negatives. Source: IAMCV dataset 2024-JKU-dataset-2.
  • Figure 3: High-level overview of the edge-segment detection pipeline. Pink indicates input and output data, while blue represents processing steps. A detailed description is provided in the text.
  • Figure 4: Illustration of 3D straight lines appearing interrupted in the image due to occlusion and discontinuous features. Examples from the Kitti dataset 2013-kitti-dataset