For decades, the checkerboard pattern has been the go-to method for camera calibration, providing only pixel-level precision. But what if we could improve accuracy even further? Discocal reveals the power of the circular pattern: a game-changer offering subpixel precision to meet challenges even from unconventional visual sensors.

Visit the official Document for details!

[Paper][Video][OpenCV Webinar][BibTex]

Discocal supports:

• RGB camera calibration
• Thermal infrared camera calibration
• Extrinsic calibration of N cameras
• RGB-TIR extrinsic calibartion
• (Comming soon) LiDAR-Camera extrinsic calibration

• 25.05.30. 📍 The uncertainty-aware version is released. It is more accurate and robust.
• 24.07.19. 🎉 This work is invited to OpenCV Webinar
• 24.06.17. 📍Add a description of how to undisort images using our method.
• 24.04.17. 📍We update circular pattern detector! Now, you don't need to tune hyperparameters for detections
• 24.04.05. 🎉 Discocal is selected for highlight poster. (11.9% of accepted papers, 2.8% of total submissions.)

Option 1) Download runfile (Easy but only works on Ubuntu PC)

• Ubuntu + x86_64 (amd64): [Download_link]

• Ubuntu + Arm64: [Download_link]

Option 2) Build with docker (Supports all architectures)

git clone https://github.com/chaehyeonsong/discocal.git
cd discocal
docker compose up --build

After build, runfiles will be created in discocal folder. You Don't have to additionally build or run docker container

Note: Revise the config file before run

• Intrinsic calibration

   sudo chmod +x run_mono && ./run_mono [config_path]

• Extrinsic calibration

   sudo chmod +x run_stereo && ./run_stereo [config_path]

You can download sample images in here

@INPROCEEDINGS{chsong-2024-cvpr,  
    author    = {Song, Chaehyeon and Shin, Jaeho and Jeon, Myung-Hwan and Lim, Jongwoo and Kim, Ayoung},
    title     = {Unbiased Estimator for Distorted Conics in Camera Calibration},
    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {373-381}
}

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

• This work is protected by a patent.
• All codes on this page are copyrighted by Seoul National University published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. You must attribute the work in the manner specified by the author. You may not use the work for commercial purposes, and you may only distribute the resulting work under the same license if you alter, transform, or create the work.
• For commercial purposes, please contact to chaehyeon@snu.ac.kr