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.

Practice

1. What is the main purpose of stereo vision in computer vision?

easy

A. To estimate the depth of objects by comparing two images

B. To enhance the color of images

C. To detect edges in a single image

D. To compress images for storage

Solution

1. Step 1: Understand stereo vision basics

   Stereo vision uses two images taken from slightly different viewpoints to find depth information.

2. Step 2: Identify the main goal

   The main goal is to estimate how far objects are by comparing their positions in the two images.

3. Final Answer:

   To estimate the depth of objects by comparing two images -> Option A

4. Quick Check:

   Stereo vision = Depth estimation [OK]

Hint: Stereo vision = depth from two images [OK]

Common Mistakes:

• Confusing stereo vision with color enhancement
• Thinking it works with only one image
• Mixing depth estimation with edge detection

2. Which of the following correctly describes 'disparity' in stereo vision?

easy

A. The difference in brightness between two images

B. The average color value of an image

C. The difference in pixel positions of the same point in two images

D. The total number of pixels in an image

Solution

1. Step 1: Define disparity in stereo vision

   Disparity is the horizontal difference in pixel positions of the same object point between the left and right images.

2. Step 2: Match the correct description

   It is not about brightness or color but about position difference to calculate depth.

3. Final Answer:

   The difference in pixel positions of the same point in two images -> Option C

4. Quick Check:

   Disparity = pixel position difference [OK]

Hint: Disparity = position difference between images [OK]

Common Mistakes:

• Confusing disparity with brightness or color
• Thinking disparity is total pixels count
• Mixing disparity with image resolution

3. Given two stereo images, the pixel of a point is at (x=150) in the left image and at (x=130) in the right image. What is the disparity value?

medium

A. 150

B. -20

C. 280

D. 20

Solution

1. Step 1: Calculate disparity from pixel positions

   Disparity = x_left - x_right = 150 - 130 = 20 pixels.

2. Step 2: Interpret the result

   Disparity is positive and represents how far the point shifted between images.

3. Final Answer:

   20 -> Option D

4. Quick Check:

   150 - 130 = 20 [OK]

Hint: Disparity = left x minus right x [OK]

Common Mistakes:

• Subtracting right from left incorrectly
• Using sum instead of difference
• Ignoring sign of disparity

4. You wrote code to compute disparity but always get zero values. What is the most likely error?

medium

A. Swapping the x and y coordinates in calculations

B. Using the same image for both left and right inputs

C. Using color images instead of grayscale

D. Calculating disparity as the sum of pixel positions

Solution

1. Step 1: Analyze zero disparity cause

   If both images are identical, the pixel positions match exactly, so disparity is zero everywhere.

2. Step 2: Check other options

   Swapping coordinates or color use won't cause all zero disparity; summing positions gives wrong values but not zero everywhere.

3. Final Answer:

   Using the same image for both left and right inputs -> Option B

4. Quick Check:

   Same images = zero disparity [OK]

Hint: Different images needed for disparity [OK]

Common Mistakes:

• Using identical images for stereo input
• Mixing x and y coordinates without correction
• Ignoring image color format effects

5. In a stereo vision system, if an object is very far away, how does its disparity value change and why?

hard

A. Disparity decreases because the pixel positions in both images become closer

B. Disparity increases because the object appears larger

C. Disparity stays the same regardless of distance

D. Disparity becomes negative because the object moves behind the cameras

Solution

1. Step 1: Understand disparity-distance relation

   Disparity is inversely related to distance; far objects have smaller disparity because their positions in both images are closer.

2. Step 2: Eliminate incorrect options

   Disparity does not increase with distance, nor does it become negative or stay constant.

3. Final Answer:

   Disparity decreases because the pixel positions in both images become closer -> Option A

4. Quick Check:

   Far object = small disparity [OK]

Hint: Farther objects have smaller disparity [OK]

Common Mistakes:

• Assuming disparity grows with distance
• Thinking disparity can be negative for far objects
• Believing disparity is constant