Awesome LIDAR-Camera calibration
A curated collection of papers, toolboxes, and notes for LiDAR-camera extrinsic calibration methods.
What is Awesome LIDAR-Camera calibration?
Awesome-LiDAR-Camera-Calibration is a curated GitHub repository that aggregates research papers, open-source toolboxes, and technical notes related to extrinsic calibration between LiDAR and camera sensors. It addresses the challenge of accurately fusing 3D point cloud data with 2D image data by providing a structured overview of calibration methodologies, from traditional target-based approaches to modern deep-learning techniques.
Researchers, engineers, and students in robotics, autonomous vehicles, and computer vision who need to implement or understand LiDAR-camera calibration for applications like sensor fusion, 3D reconstruction, or navigation systems.
It saves significant time by centralizing scattered resources into a single, well-organized repository, offering both theoretical insights (via paper summaries) and practical tools (via linked toolboxes) for immediate implementation.
Overview
A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes
Use Cases
Best For
- Researchers surveying state-of-the-art LiDAR-camera calibration methods
- Engineers implementing sensor fusion for autonomous driving systems
- Robotics teams needing to calibrate multi-sensor setups (LiDAR + camera)
- Academic courses or workshops on sensor calibration and 3D perception
- Developers evaluating open-source calibration toolboxes like OpenCalib or CamLaserCalibraTool
- Practitioners comparing target-based vs. targetless calibration approaches
Not Ideal For
- Teams needing a turnkey calibration solution with GUI and automation
- Projects requiring real-time, online calibration for dynamic environments
- Beginners seeking step-by-step tutorials with code examples
- Organizations looking for a maintained, single-codebase calibration library
Pros & Cons
Pros
Categorizes calibration methods into target-based, targetless, and deep-learning approaches, making it easy for users to navigate and select relevant techniques based on their needs.
Provides tables summarizing key papers with features, optimization techniques, and toolbox links, saving researchers time by centralizing academic resources in one place.
Aggregates open-source implementations like CamLaserCalibraTool and OpenCalib, offering practical starting points for engineers to implement calibration without scouring multiple repositories.
Covers both traditional (e.g., checkerboard) and modern (e.g., Segment Anything) techniques, ensuring a comprehensive overview of advancements in LiDAR-camera fusion.
Cons
The repository only curates links to external toolboxes, forcing users to deal with varying documentation, setups, and maintenance issues across different projects.
Emphasizes paper summaries over hands-on tutorials, so engineers must seek additional resources for implementation details, debugging, or deployment workflows.
As a curated list, it may not be regularly updated, risking broken links or obsolete methods in a fast-evolving field like autonomous systems calibration.
Frequently Asked Questions
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