LiDAR data processing basics in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load LiDAR point cloud data from a file.

Computer Vision

import open3d as o3d

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

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A"image.png"

B"data.txt"

C"data.las"

D"video.mp4"

Attempts:

3 left

💡 Hint

Common Mistakes

Using image or video file names instead of point cloud files.

Forgetting to put the filename in quotes.

LiDAR point cloud data is often stored in .las files. The function read_point_cloud loads this data.

2fill in blank

medium

Complete the code to visualize the loaded LiDAR point cloud.

Computer Vision

import open3d as o3d

pcd = o3d.io.read_point_cloud("data.las")
o3d.visualization.[1]([pcd])

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Adraw_geometries

Bplot

Cshow

Ddisplay

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect visualization function names.

Forgetting to import the visualization module.

The function draw_geometries is used in Open3D to visualize point clouds.

3fill in blank

hard

Fix the error in the code to compute the normals of the point cloud.

Computer Vision

pcd = o3d.io.read_point_cloud("data.las")
pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParam[1](radius=0.1, max_nn=30))

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ARadius

Bradius

Cradius_search

DRadiusSearch

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase or incorrect class names.

Misspelling the class name.

The correct class name is KDTreeSearchParamRadius.

4fill in blank

hard

Fill both blanks to create a voxel grid downsampling of the point cloud.

Computer Vision

pcd = o3d.io.read_point_cloud("data.las")
downsampled_pcd = pcd.[1](voxel_size=[2])

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Avoxel_down_sample

Bdownsample

Cvoxel_size

D0.05

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong method names like 'downsample'.

Passing voxel size as a string instead of a float.

The method voxel_down_sample downsamples the point cloud using the given voxel size.

5fill in blank

hard

Fill all three blanks to filter points based on height (z coordinate) in the point cloud.

Computer Vision

points = np.asarray(pcd.points)
filtered_points = points[points[:, [1]] [2] [3]]
filtered_pcd = o3d.geometry.PointCloud()
filtered_pcd.points = o3d.utility.Vector3dVector(filtered_points)

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A2

B>

C1.5

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong coordinate index for z.

Using wrong comparison operator.

Using a string instead of a number for height.

The z coordinate is at index 2, filtering points where z > 1.5.