Stereo vision concept in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is stereo vision in computer vision?

Stereo vision is a technique that uses two cameras to capture images from slightly different viewpoints to estimate depth and 3D structure of a scene.

beginner

What is disparity in stereo vision?

Disparity is the difference in the position of the same object point in the left and right images. It helps calculate the depth of that point.

intermediate

Why do stereo cameras need to be calibrated?

Calibration finds the exact positions and orientations of the two cameras and corrects lens distortions, so depth calculations from disparity are accurate.

intermediate

How is depth calculated from disparity in stereo vision?

Depth is inversely proportional to disparity. The formula is Depth = (Baseline × Focal Length) / Disparity, where baseline is the distance between cameras.

intermediate

What is a common challenge in stereo vision?

Matching points between two images can be hard in areas with low texture or repetitive patterns, causing errors in disparity and depth estimation.

What does stereo vision primarily help to estimate?

A Speed of objects

B Color of objects

C Depth of objects

D Temperature of objects

Stereo vision uses two images to estimate how far objects are, which is depth.

What is disparity in stereo vision?

A Difference in object color

B Difference in object position between two images

C Difference in camera brightness

D Difference in image resolution

Disparity is the difference in position of the same point seen by two cameras.

Why is camera calibration important in stereo vision?

A To find camera positions and fix distortions

B To increase image size

C To adjust image colors

D To speed up image capture

Calibration finds camera positions and corrects lens distortions for accurate depth.

Which formula relates depth to disparity?

A Depth = Disparity × Baseline

B Depth = Focal Length / Baseline

C Depth = Disparity / Focal Length

D Depth = (Baseline × Focal Length) / Disparity

Depth is calculated by dividing the product of baseline and focal length by disparity.

What makes matching points between stereo images difficult?

A Low texture or repetitive patterns

B High texture areas

C Bright lighting

D Large camera distance

Low texture or repetitive patterns make it hard to find matching points.

Explain how stereo vision uses two images to estimate depth.

Think about how the difference in object position between two images helps find how far it is.

Describe the challenges faced in stereo vision and how calibration helps.

Consider what makes matching hard and why knowing camera setup is important.