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Computer Vision
Image Processing
Feature Detection
Object Detection
Object Tracking
Face Recognition
Image Classification
Semantic Segmentation
3D Computer Vision
Object Recognition in 3D
Gesture and Hand Tracking
Scene Understanding
3D Computer Vision Quiz Questions
1.
What is the primary goal of 3D computer vision?
A. Noise reduction
B. Image resizing
C. Extracting three-dimensional information from 2D images
D. Color correction
view answer:
C. Extracting three-dimensional information from 2D images
Explanation:
The primary goal of 3D computer vision is to extract three-dimensional information from 2D images or scenes.
2.
Which technique is commonly used in 3D computer vision to estimate the depth of objects in a scene based on the disparity between stereo images?
A. Histogram equalization
B. Stereo vision
C. Median filtering
D. Sobel operator
view answer:
B. Stereo vision
Explanation:
Stereo vision is commonly used in 3D computer vision to estimate object depth based on the disparity between stereo images.
3.
In 3D computer vision, what does "structure from motion" (SfM) refer to?
A. Noise reduction
B. Color correction
C. Reconstructing 3D scene structure from 2D motion of a camera
D. Image resizing
view answer:
C. Reconstructing 3D scene structure from 2D motion of a camera
Explanation:
Structure from motion (SfM) is the process of reconstructing 3D scene structure from the 2D motion of a camera.
4.
What is the primary challenge in 3D computer vision when dealing with occluded objects and cluttered scenes?
A. Image resizing
B. Color correction
C. Noise reduction
D. Accurate object localization and tracking
view answer:
D. Accurate object localization and tracking
Explanation:
Accurate object localization and tracking is a primary challenge in 3D computer vision, especially in occluded or cluttered scenes.
5.
Which sensor is commonly used for capturing depth information in 3D computer vision applications, providing a depth map of a scene?
A. LiDAR
B. Histogram equalization
C. Median filtering
D. Sobel operator
view answer:
A. LiDAR
Explanation:
LiDAR (Light Detection and Ranging) is commonly used to capture depth information in 3D computer vision applications.
6.
In 3D computer vision, what is the primary purpose of "triangulation" in stereo vision?
A. Noise reduction
B. Color correction
C. Estimating the 3D position of a point in the scene
D. Image resizing
view answer:
C. Estimating the 3D position of a point in the scene
Explanation:
Triangulation in stereo vision is used to estimate the 3D position of a point in the scene.
7.
What is the primary advantage of using "depth sensors" like Microsoft Kinect in 3D computer vision?
A. Improved image resizing capabilities
B. Noise reduction
C. The ability to directly capture depth information in real-time
D. Precise color correction
view answer:
C. The ability to directly capture depth information in real-time
Explanation:
Depth sensors like Microsoft Kinect have the advantage of directly capturing depth information in real-time for 3D computer vision.
8.
In 3D computer vision, what does "SLAM" stand for?
A. Simultaneous Localization and Mapping
B. Noise reduction
C. Image resizing
D. Color correction
view answer:
A. Simultaneous Localization and Mapping
Explanation:
SLAM stands for Simultaneous Localization and Mapping, a technique used in 3D computer vision to build maps while tracking the camera's position.
9.
What is the primary role of "point cloud processing" in 3D computer vision?
A. Noise reduction
B. Color correction
C. Processing and analyzing 3D point cloud data
D. Image resizing
view answer:
C. Processing and analyzing 3D point cloud data
Explanation:
Point cloud processing in 3D computer vision involves processing and analyzing 3D point cloud data.
10.
Which type of camera is often used in 3D computer vision for capturing depth information based on the time-of-flight principle?
A. Histogram equalization
B. Median filtering
C. Time-of-flight camera
D. Sobel operator
view answer:
C. Time-of-flight camera
Explanation:
Time-of-flight cameras are used in 3D computer vision to capture depth information based on the time it takes for light to travel to objects and back.
11.
What is the primary challenge in 3D computer vision when dealing with dynamic scenes and moving objects?
A. Image resizing
B. Color correction
C. Noise reduction
D. Object motion analysis and separation
view answer:
D. Object motion analysis and separation
Explanation:
Object motion analysis and separation are primary challenges in 3D computer vision when dealing with dynamic scenes and moving objects.
12.
Which 3D computer vision technique is used for creating a 3D model of a scene by projecting multiple 2D images onto a common 3D space?
A. Stereo vision
B. Structure from motion (SfM)
C. Median filtering
D. Histogram equalization
view answer:
B. Structure from motion (SfM)
Explanation:
Structure from motion (SfM) is used to create a 3D model by projecting multiple 2D images onto a common 3D space in 3D computer vision.
13.
What is the primary purpose of "3D object recognition" in 3D computer vision?
A. Noise reduction
B. Color correction
C. Identifying and classifying objects in the 3D scene
D. Image resizing
view answer:
C. Identifying and classifying objects in the 3D scene
Explanation:
3D object recognition in 3D computer vision involves identifying and classifying objects in the 3D scene.
14.
Which sensor technology is commonly used in 3D computer vision for measuring distances by emitting and measuring reflected laser pulses?
A. Histogram equalization
B. Median filtering
C. LiDAR (Light Detection and Ranging)
D. Sobel operator
view answer:
C. LiDAR (Light Detection and Ranging)
Explanation:
LiDAR (Light Detection and Ranging) technology is commonly used for measuring distances in 3D computer vision.
15.
In 3D computer vision, what is the primary goal of "semantic 3D reconstruction"?
A. Noise reduction
B. Color correction
C. Reconstructing the 3D scene while assigning semantic labels to objects and regions
D. Image resizing
view answer:
C. Reconstructing the 3D scene while assigning semantic labels to objects and regions
Explanation:
Semantic 3D reconstruction aims to reconstruct the 3D scene while assigning semantic labels to objects and regions.
16.
What is the primary advantage of "time-of-flight" cameras in 3D computer vision applications?
A. Accurate depth measurement at high frame rates
B. Improved image resizing capabilities
C. Noise reduction
D. Precise color correction
view answer:
A. Accurate depth measurement at high frame rates
Explanation:
Time-of-flight cameras offer accurate depth measurement at high frame rates in 3D computer vision applications.
17.
In 3D computer vision, what is the primary role of "3D object tracking"?
A. Noise reduction
B. Color correction
C. Monitoring and tracking the 3D motion of objects in the scene
D. Image resizing
view answer:
C. Monitoring and tracking the 3D motion of objects in the scene
Explanation:
3D object tracking involves monitoring and tracking the 3D motion of objects in the scene.
18.
What is the primary challenge in 3D computer vision when dealing with low-textured or textureless surfaces?
A. Image resizing
B. Color correction
C. Noise reduction
D. Feature extraction and matching
view answer:
D. Feature extraction and matching
Explanation:
Feature extraction and matching are challenges in 3D computer vision when dealing with low-textured or textureless surfaces.
19.
Which technique in 3D computer vision is used for capturing depth information by projecting a structured light pattern onto a scene and analyzing its deformation?
A. Histogram equalization
B. Structured light
C. Median filtering
D. Sobel operator
view answer:
B. Structured light
Explanation:
Structured light is used to capture depth information in 3D computer vision by projecting and analyzing a structured light pattern.
20.
What is the primary purpose of "3D point cloud registration" in 3D computer vision?
A. Noise reduction
B. Color correction
C. Aligning and merging multiple 3D point clouds into a common coordinate system
D. Image resizing
view answer:
C. Aligning and merging multiple 3D point clouds into a common coordinate system
Explanation:
3D point cloud registration involves aligning and merging multiple 3D point clouds into a common coordinate system.
21.
In 3D computer vision, what is the primary goal of "voxel-based 3D reconstruction"?
A. Noise reduction
B. Color correction
C. Reconstructing the 3D scene using a volumetric grid of voxels
D. Image resizing
view answer:
C. Reconstructing the 3D scene using a volumetric grid of voxels
Explanation:
Voxel-based 3D reconstruction reconstructs the 3D scene using a volumetric grid of voxels in 3D computer vision.
22.
What is the primary advantage of using "semantic segmentation" in 3D computer vision for scene understanding?
A. Improved image resizing capabilities
B. Noise reduction
C. Assigning semantic labels to 3D regions and objects
D. Precise color correction
view answer:
C. Assigning semantic labels to 3D regions and objects
Explanation:
Semantic segmentation in 3D computer vision assigns semantic labels to 3D regions and objects for scene understanding.
23.
In 3D computer vision, what is the primary challenge when dealing with 3D object recognition in unstructured environments?
A. Image resizing
B. Color correction
C. Noise reduction
D. Handling occlusions, variations in lighting, and complex backgrounds
view answer:
D. Handling occlusions, variations in lighting, and complex backgrounds
Explanation:
Handling occlusions, variations in lighting, and complex backgrounds is a challenge in 3D object recognition in unstructured environments.
24.
Which type of sensor is commonly used for real-time 3D depth sensing in handheld devices like smartphones and tablets?
A. Time-of-flight sensor
B. Histogram equalization
C. Median filtering
D. Sobel operator
view answer:
A. Time-of-flight sensor
Explanation:
Time-of-flight sensors are commonly used for real-time 3D depth sensing in handheld devices.
25.
In 3D computer vision, what is the primary role of "bundle adjustment" in the context of multi-view geometry?
A. Noise reduction
B. Color correction
C. Refining camera poses and scene structure for improved reconstruction
D. Image resizing
view answer:
C. Refining camera poses and scene structure for improved reconstruction
Explanation:
Bundle adjustment refines camera poses and scene structure to improve 3D reconstruction in multi-view geometry.
26.
What is the primary goal of "3D pose estimation" in 3D computer vision?
A. Noise reduction
B. Color correction
C. Determining the 3D position and orientation of an object in the scene
D. Image resizing
view answer:
C. Determining the 3D position and orientation of an object in the scene
Explanation:
3D pose estimation aims to determine the 3D position and orientation of an object in the scene.
27.
In 3D computer vision, what is the primary challenge when dealing with large-scale 3D scene reconstruction?
A. Image resizing
B. Color correction
C. Noise reduction
D. Efficient storage and processing of massive 3D data
view answer:
D. Efficient storage and processing of massive 3D data
Explanation:
Efficient storage and processing of massive 3D data are challenges in large-scale 3D scene reconstruction.
28.
Which 3D computer vision technique is used to create a 3D model of a scene by projecting patterns of light and capturing their deformation?
A. Histogram equalization
B. Structured light scanning
C. Median filtering
D. Sobel operator
view answer:
B. Structured light scanning
Explanation:
Structured light scanning involves projecting patterns of light and capturing their deformation to create a 3D model in 3D computer vision.
29.
What is the primary purpose of "3D registration" in 3D computer vision?
A. Noise reduction
B. Color correction
C. Aligning and merging 3D data from different sensors or viewpoints
D. Image resizing
view answer:
C. Aligning and merging 3D data from different sensors or viewpoints
Explanation:
3D registration aligns and merges 3D data from different sensors or viewpoints in 3D computer vision.
30.
In 3D computer vision, what is the primary advantage of "3D convolutional neural networks" (3D CNNs)?
A. Improved image resizing capabilities
B. Noise reduction
C. Extracting spatial features from 3D data for tasks like object recognition
D. Precise color correction
view answer:
C. Extracting spatial features from 3D data for tasks like object recognition
Explanation:
3D convolutional neural networks (3D CNNs) extract spatial features from 3D data, making them suitable for tasks like object recognition in 3D computer vision.
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