[AI from Scratch] Episode 295: Fundamentals of 3D Vision — Handling Depth Information

Recap and Today’s Theme

Hello! In the previous episode, we discussed evaluation metrics in computer vision, such as accuracy, IoU, and mAP, which are crucial for evaluating model performance in various tasks.

Today, we will explore the basics of 3D vision and learn how to handle depth information. 3D vision enables the understanding of three-dimensional structures from images, which is widely used in fields such as robotics, autonomous driving, and VR/AR. We will cover the fundamental concepts of 3D vision, methods for obtaining depth information, and their practical applications.

What is 3D Vision?

3D vision is a field of computer vision that involves adding depth information (distance or depth) to 2D images, allowing for the three-dimensional analysis of objects and scenes. By using 3D vision, computers can better understand the shape, location, and spatial arrangement of objects in the environment, which can be applied in tasks such as navigation and manipulation.

Main Applications of 3D Vision

• Autonomous Driving: Detects the position and distance of surrounding vehicles and obstacles in real-time to assist in navigation.
• Robotics: Allows robots to recognize objects and manipulate them accurately by utilizing 3D information.
• Architecture and Surveying: Generates 3D models of buildings and terrain for design and analysis.
• VR/AR: Recreates the surrounding environment in 3D, providing an immersive experience.

Methods for Obtaining Depth Information

To implement 3D vision, we need to acquire depth information using cameras and sensors. The following are common methods used to obtain depth:

1. Stereo Vision

Stereo vision uses two cameras to capture the scene from different angles and calculate depth based on the difference between the two images.

• Principle:
• Two cameras (stereo cameras) capture the scene simultaneously from slightly different positions, resulting in slight shifts (disparity) between the two images.
• By analyzing this disparity, the depth of each pixel can be calculated using triangulation.
• Advantages:
• Low cost, as it only requires cameras to capture depth information.
• Disadvantages:
• Struggles with textureless scenes or reflective surfaces where disparity cannot be accurately measured.

2. LiDAR (Light Detection and Ranging)

LiDAR uses laser beams to measure depth. It is widely used in autonomous vehicles and robotics for precise distance measurements.

• Principle:
• A laser beam is emitted towards the scene, and the time it takes for the reflected light to return is measured. This time of flight is then used to calculate the distance to objects with high precision.
• Advantages:
• High accuracy even in low-light conditions.
• Disadvantages:
• Expensive and requires complex setup.

3. ToF Camera (Time of Flight)

A ToF camera uses infrared light pulses to measure the depth of objects based on the time it takes for the reflected light to return.

• Principle:
• Infrared light is emitted, and the reflection time is measured for each pixel to generate depth information.
• Advantages:
• Can capture real-time depth data and is compact enough to be used in consumer devices.
• Disadvantages:
• Lower accuracy compared to LiDAR, and performance can degrade in bright sunlight or on reflective surfaces.

4. Structured Light

Structured light projects a pattern (such as a grid or dot array) onto the scene, and the distortion of this pattern is used to calculate depth. It is commonly used in 3D scanners.

• Principle:
• The projected pattern is deformed when it hits an object. By analyzing how the pattern changes, depth information is calculated.
• Advantages:
• High accuracy in capturing detailed 3D shapes.
• Disadvantages:
• Performance drops with moving objects or challenging lighting conditions.

Example: Implementing Stereo Vision in Python

We can use Python with OpenCV to create a depth map using stereo vision. Below is an example of how to generate a depth map from stereo images using block matching.

Required Libraries Installation

pip install opencv-python

Stereo Vision Implementation

Here’s an example code to generate a depth map from two stereo images:

import cv2
import numpy as np

# Load left and right stereo images
img_left = cv2.imread('left_image.jpg', 0)
img_right = cv2.imread('right_image.jpg', 0)

# Create stereo block matching object
stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15)

# Compute the disparity (depth map)
disparity = stereo.compute(img_left, img_right)

# Display the depth map
cv2.imshow('Disparity Map', disparity)
cv2.waitKey(0)
cv2.destroyAllWindows()

Explanation of Code

• cv2.imread(): Reads the left and right images in grayscale mode.
• cv2.StereoBM_create(): Initializes the stereo block matching algorithm to compute the disparity map (depth map).
• disparity = stereo.compute(): Calculates the disparity map based on the stereo images, which is then displayed.

By executing this code, you can generate a depth map based on stereo images. Objects closer to the camera will have higher disparity, while distant objects will have lower disparity.

Challenges and Limitations of 3D Vision

While 3D vision offers numerous advantages, it also has challenges:

1. Dependency on Environmental Conditions: The accuracy of depth information is affected by lighting conditions and reflective surfaces, particularly in stereo vision and structured light methods.
2. Computational Load: Calculating accurate depth maps requires significant computational power, especially for real-time applications, where GPUs or specialized hardware may be needed.
3. Noise in Data: Depth data obtained from sensors or stereo cameras can be noisy, requiring post-processing and filtering to improve accuracy.

Summary

In this episode, we explored the basics of 3D vision, focusing on methods for obtaining depth information, such as stereo vision, LiDAR, and ToF cameras. These methods are used in a wide range of applications, including autonomous driving, robotics, and architecture. By understanding the fundamentals of 3D vision, you can apply these techniques to various real-world scenarios.

Next Episode Preview

In the next episode, we will dive into point cloud data processing, learning how to handle and analyze 3D scan data for more advanced 3D vision tasks.

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Notes

• LiDAR: A sensor technology that uses laser beams to measure distances with high precision.
• Stereo Vision: A method of obtaining depth information using two cameras and calculating the disparity between the images they capture【635†source】.