An Invitation to 3D Vision is an introductory tutorial on 3D computer vision (a.k.a. geometric vision or visual geometry or multiple-view geometry). It aims to help beginners understand the basic theories of 3D vision and implement practical applications using OpenCV. In addition to tutorial slides, example codes are provided for educational purposes. They include simple but interesting and practical applications. The example codes are kept as short as possible (mostly less than 100 lines) to be clear and easy to understand.

• To clone this repository (codes and slides): git clone https://github.com/mint-lab/3dv_tutorial.git
• To fork this repository to your GitHub account: Click here
• To download codes and slides as a ZIP file: Click here
• 📝 How to run example codes in Python
• 📝 How to run example codes in C++

Where does the name come from?

• The main title, An Invitation to 3D Vision, came from a legendary book by Yi Ma, Stefano Soatto, Jana Kosecka, and Shankar S. Sastry. We wish that our tutorial will be the first gentle invitation card for beginners to 3D vision and its applications.
• The subtitle, for everyone, was inspired by Prof. Kim's online lecture (in Korean). Our tutorial is also intended not only for students and researchers in academia, but also for hobbyists and developers in industry. We tried to describe important and typical problems and their solutions in OpenCV. We hope readers can understand it easily without a strong mathematical background.

Lecture Slides

• Section 1. Introduction
• Section 2. Single-view Geometry
• Section 3. Two-view Geometry
• Section 4. Solving Problems
• Section 5. Finding Correspondence
• Section 6. Multiple-view Geometry
• Special Topic) Bayesian Filtering
• Section 7. Visual SLAM and Odometry
• Section 8. 3D Representations

Example Codes

• Section 1. Introduction [slides]
• Section 2. Single-view Geometry [slides]
  • Getting Started with 2D
    • 3D rotation conversion [python]
  • Pinhole Camera Model
    • Object localization [python] [cpp]
    • Image formation [python] [cpp]
  • Geometric Distortion Models
    • Geometric distortion visualization [python]
    • Geometric distortion correction [python] [cpp] [result video]
    • Geometric distortion correction with virtual PTZ [python]
  • Camera Calibration
    • Camera calibration [python] [cpp]
  • Absolute Camera Pose Estimation (a.k.a. perspective-n-point; PnP)
    • Pose estimation (chessboard) [python] [cpp] [result video]
    • Pose estimation (book) [python] [cpp]
    • Pose estimation (book) with camera calibration [python] [cpp]
    • Pose estimation (book) with camera calibration without initial