Table of Contents
Calculating depth from stereo images is a fundamental process in computer vision and robotics. It involves analyzing two images captured from slightly different viewpoints to determine the distance of objects within a scene. This guide provides a clear, step-by-step overview of the process for engineers and developers.
Understanding Stereo Image Geometry
Stereo imaging uses two cameras positioned at a known distance apart, called the baseline. The key concept is the disparity, which is the difference in the position of an object’s image between the two views. Calculating disparity is the first step toward depth estimation.
Steps to Calculate Depth
The process involves several stages, including image rectification, disparity computation, and depth calculation.
1. Image Rectification
This step aligns the stereo images so that corresponding points are on the same horizontal line, simplifying disparity calculation. It involves transforming images based on camera calibration data.
2. Disparity Map Generation
Using algorithms such as block matching or semi-global matching, the disparity for each pixel is computed. The result is a disparity map indicating the pixel differences between the two images.
Calculating Depth from Disparity
Depth is inversely proportional to disparity. The formula used is:
Depth = (focal length × baseline) / disparity
Where the focal length and baseline are known parameters from camera calibration. This calculation provides the distance from the camera to each point in the scene.
Applications and Considerations
Depth estimation from stereo images is used in autonomous vehicles, 3D reconstruction, and robotic navigation. Accurate calibration and proper algorithm selection are essential for reliable results.