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
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