3D object detection in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a 3D point cloud from a file.

Computer Vision

import open3d as o3d

point_cloud = o3d.io.read_point_cloud([1])
print(point_cloud)

Drag options to blanks, or click blank then click option'

Apointcloud

B12345

CNone

D"data/pointcloud.pcd"

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a number instead of a string path.

Forgetting to put quotes around the file path.

The read_point_cloud function requires a string path to the point cloud file.

2fill in blank

medium

Complete the code to create a 3D bounding box from a point cloud.

Computer Vision

bbox = point_cloud.get_axis_aligned_bounding_box()
print(bbox.[1]())

Drag options to blanks, or click blank then click option'

Aarea

Bvolume

Clength

Dcenter

Attempts:

3 left

💡 Hint

Common Mistakes

Using area() which is for 2D shapes.

Using center() which returns a point, not a size.

The volume() method returns the volume of the bounding box.

3fill in blank

hard

Fix the error in the code to visualize the point cloud with bounding box.

Computer Vision

import open3d as o3d

vis = o3d.visualization.Visualizer()
vis.create_window()
vis.add_geometry(point_cloud)
vis.add_geometry([1])
vis.run()
vis.destroy_window()

Drag options to blanks, or click blank then click option'

Abbox

BNone

Co3d.geometry.PointCloud()

Dpoint_cloud

Attempts:

3 left

💡 Hint

Common Mistakes

Adding the point cloud twice instead of the bounding box.

Adding None or creating a new empty geometry.

The bounding box object 'bbox' must be added to the visualizer to show it.

4fill in blank

hard

Fill both blanks to filter points inside the bounding box.

Computer Vision

points = np.asarray(point_cloud.points)
mask = (points[:, [1]] >= bbox.min_bound[[2]]) & (points[:, [1]] <= bbox.max_bound[[2]])
filtered_points = points[mask]

Drag options to blanks, or click blank then click option'

A0

B1

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using different indices for points and bounding box bounds.

Using an invalid axis index like 3.

To filter along the x-axis, use index 0 for both point coordinates and bounding box bounds.

5fill in blank

hard

Fill all three blanks to train a simple 3D object detection model using PyTorch.

Computer Vision

import torch
import torch.nn as nn

class Simple3DDetector(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear([1], 64)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(64, [2])

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = Simple3DDetector()
input_tensor = torch.randn(10, [3])
output = model(input_tensor)
print(output.shape)

Drag options to blanks, or click blank then click option'

A3

B2

C5

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Mismatching input tensor size with model input layer.

Wrong output size for classification.

Input features are 3 (x,y,z), output classes 2 (object or no object), input tensor batch size 10 with 3 features.

Practice

1. What is the main goal of 3D object detection in computer vision?

easy

A. To classify images into categories

B. To find and locate objects in three-dimensional space

C. To enhance image colors

D. To compress video files

Solution

1. Step 1: Understand 3D object detection purpose

   3D object detection aims to find objects and their positions in 3D space, unlike simple image classification.

2. Step 2: Compare options to definition

   Only To find and locate objects in three-dimensional space describes locating objects in 3D space, which matches the goal of 3D object detection.

3. Final Answer:

   To find and locate objects in three-dimensional space -> Option B

4. Quick Check:

   3D object detection = locating objects in 3D space [OK]

Hint: 3D detection means finding objects in 3D space, not just classifying [OK]

Common Mistakes:

• Confusing 3D detection with image classification
• Thinking it changes image colors
• Assuming it compresses data

2. Which of the following is the correct way to represent a 3D bounding box in code?

easy

A. A 2D rectangle with width and height only

B. A single number representing volume

C. A color code string like '#FF0000'

D. A list of 8 corner points with (x, y, z) coordinates

Solution

1. Step 1: Recall 3D bounding box structure

   A 3D bounding box is defined by its 8 corners in 3D space, each with (x, y, z) coordinates.

2. Step 2: Evaluate options

   Only A list of 8 corner points with (x, y, z) coordinates correctly describes this. Options A, B, and D do not represent 3D bounding boxes properly.

3. Final Answer:

   A list of 8 corner points with (x, y, z) coordinates -> Option D

4. Quick Check:

   3D box = 8 corners with (x,y,z) [OK]

Hint: 3D boxes need 8 corners, not just volume or 2D shapes [OK]

Common Mistakes:

• Using only 2D rectangles for 3D boxes
• Confusing volume with box representation
• Using color codes instead of coordinates

3. Given the following Python code snippet for a simple 3D object detection model output, what will be the printed prediction?

predictions = {'car': [1.2, 3.4, 0.5], 'pedestrian': [2.1, 1.0, 0.3]}
print(predictions['car'])

medium

A. [1.2, 3.4, 0.5]

B. [2.1, 1.0, 0.3]

C. 'car'

D. KeyError

Solution

1. Step 1: Understand dictionary access in Python

   Accessing predictions['car'] returns the value associated with the key 'car', which is the list [1.2, 3.4, 0.5].

2. Step 2: Confirm output of print statement

   The print statement outputs the list [1.2, 3.4, 0.5], so [1.2, 3.4, 0.5] is correct.

3. Final Answer:

   [1.2, 3.4, 0.5] -> Option A

4. Quick Check:

   Dictionary access by key returns its value [OK]

Hint: Dictionary[key] returns the value for that key in Python [OK]

Common Mistakes:

• Confusing keys and values
• Expecting a KeyError without reason
• Printing the key instead of the value

4. The following code attempts to calculate the center of a 3D bounding box but has an error. What is the error?

def center_of_box(corners):
    x = (corners[0][0] + corners[1][0] + corners[2][0] + corners[3][0]) / 4
    y = (corners[0][1] + corners[1][1] + corners[2][1] + corners[3][1]) / 4
    z = (corners[0][2] + corners[1][2] + corners[2][2] + corners[3][2]) / 4
    return (x, y, z)

box_corners = [(1,2,3), (3,2,3), (3,4,3), (1,4,3), (1,2,5), (3,2,5), (3,4,5), (1,4,5)]
print(center_of_box(box_corners))

medium

A. The box_corners list has incorrect data types

B. The function uses wrong indices for coordinates

C. Only 4 corners are averaged instead of all 8

D. The function returns a list instead of a tuple

Solution

1. Step 1: Analyze the function's averaging method

   The function averages only the first 4 corners, ignoring the last 4 corners of the 3D box.

2. Step 2: Understand 3D box center calculation

   To find the true center, all 8 corners must be averaged, so the function misses half the points.

3. Final Answer:

   Only 4 corners are averaged instead of all 8 -> Option C

4. Quick Check:

   Center needs all 8 corners averaged [OK]

Hint: Average all 8 corners for center, not just 4 [OK]

Common Mistakes:

• Averaging only part of the corners
• Mixing up coordinate indices
• Confusing tuples and lists (not an error here)

5. In a 3D object detection system for self-driving cars, which metric best measures how well the predicted 3D bounding boxes match the true boxes?

hard

A. Intersection over Union (IoU) in 3D space

B. Pixel accuracy on 2D images

C. Mean Squared Error of RGB colors

D. Number of detected objects only

Solution

1. Step 1: Understand evaluation metrics for 3D detection

   IoU measures overlap between predicted and true boxes, extended to 3D for volume overlap.

2. Step 2: Compare other options

   Pixel accuracy and color errors do not measure 3D box quality; counting objects ignores box accuracy.

3. Final Answer:

   Intersection over Union (IoU) in 3D space -> Option A

4. Quick Check:

   3D IoU = best metric for 3D box accuracy [OK]

Hint: Use 3D IoU to measure box overlap accuracy [OK]

Common Mistakes:

• Using 2D pixel accuracy for 3D boxes
• Confusing color error with box accuracy
• Ignoring box overlap quality