Point cloud processing in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Point Cloud Normal Estimation

Which method is commonly used to estimate normals in a point cloud for surface reconstruction?

A Applying k-means clustering to segment the point cloud

B Applying Fourier transform on the entire point cloud

C Using histogram equalization on point intensities

D Using Principal Component Analysis (PCA) on the local neighborhood of each point

💡 Hint

Think about how to find the main direction of variation in a small group of points.

PCA finds the main directions of variance in a local neighborhood, which helps estimate the surface normal as the direction with least variance.

❓ Predict Output

intermediate

Output of Point Cloud Downsampling Code

What is the number of points after running this voxel grid downsampling code on a point cloud with 10000 points and voxel size 0.1?

Computer Vision

import open3d as o3d
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector([[x*0.05, y*0.05, z*0.05] for x in range(20) for y in range(20) for z in range(25)])
downsampled = pcd.voxel_down_sample(voxel_size=0.1)
num_points = len(downsampled.points)
print(num_points)

A 4000

B 5000

C 2000

D 10000

💡 Hint

Consider how many voxels fit in the bounding box when voxel size is 0.1 and points are spaced by 0.05.

The original points are spaced 0.05 apart, so each voxel of size 0.1 contains roughly 8 points. The total voxels are about 2000, so downsampled points are 2000.

❓ Model Choice

advanced

Choosing a Model for Point Cloud Classification

You want to classify objects from raw 3D point clouds with varying point counts and no fixed grid. Which model architecture is best suited?

A 3D Convolutional Neural Network on voxelized grid

B PointNet architecture using symmetric functions on points

C Fully connected network on flattened point coordinates

D Recurrent Neural Network processing points sequentially

💡 Hint

Think about models that handle unordered sets and varying input sizes.

PointNet uses symmetric functions like max pooling to handle unordered point sets and varying sizes, making it ideal for raw point cloud classification.

❓ Metrics

advanced

Evaluating Point Cloud Segmentation Quality

Which metric best measures the quality of semantic segmentation on a point cloud where each point is assigned a class label?

A Mean Intersection over Union (mIoU) over all classes

B Root Mean Square Error (RMSE) of point coordinates

C Silhouette score of clustered points

D Accuracy of predicted bounding box coordinates

💡 Hint

Consider a metric that compares predicted and true class labels per point across classes.

mIoU measures overlap between predicted and true class regions per class, averaged over all classes, making it suitable for semantic segmentation evaluation.

🔧 Debug

expert

Debugging Point Cloud Registration Code

Given this code snippet for point cloud registration, what error will it raise?

Computer Vision

import open3d as o3d
import numpy as np
source = o3d.io.read_point_cloud('source.pcd')
target = o3d.io.read_point_cloud('target.pcd')
threshold = 0.02
trans_init = np.eye(4)
reg_p2p = o3d.pipelines.registration.registration_icp(
    source, target, threshold, trans_init,
    o3d.pipelines.registration.TransformationEstimationPointToPoint())
print(reg_p2p.transformation)

A NameError: name 'np' is not defined

B FileNotFoundError: source.pcd not found

C TypeError: registration_icp() missing required positional argument

D AttributeError: 'PointCloud' object has no attribute 'read_point_cloud'

💡 Hint

Check if all necessary libraries are imported before use.

The code uses 'np.eye(4)' but numpy (np) is not imported, causing a NameError.