Point cloud classification with machine learning: A short guide

Have you ever wondered how self-driving cars "see" the world? Or how robots understand 3D spaces? One key technology behind this magic is called point cloud classification. It might sound complicated, but don’t worry—we’re going to break it down into simple, fun chunks. By the end of this post, you’ll know what a point cloud is, how machine learning (ML) helps us make sense of it, and even how you could try it yourself!

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What Is a Point Cloud?

Imagine you're standing in a field, taking millions of tiny measurements all around you—up, down, left, right, and everything in between. Each measurement records a single point in space. If you did this enough times, you'd get a giant cloud of dots that represent the world around you. That’s a point cloud!

More formally:

• A point cloud is a collection of 3D points (x, y, z), usually generated by 3D scanners like LiDAR or depth cameras.

This image shows a 3D point cloud with distance measurements between key points. Blue and yellow dots represent scanned surfaces, while red and yellow lines highlight dimensions like width and height. It's a tool used in construction or 3D mapping for spatial analysis.

• Each point can also have features like color (RGB), intensity, or even time.

  
  

This image shows an RGB-colored point cloud, where each point in 3D space carries color information (red, green, blue). The colors help visualize real-world surfaces more clearly, adding context to the raw geometry. Such point clouds are often generated from photogrammetry or LiDAR combined with camera data, making them useful for tasks like object recognition, mapping, and scene reconstruction.

🌟 Why Classify Point Clouds?

Once we have a point cloud, we need to understand what the points represent. Are they part of a car, a tree, a person, or the road?

Point cloud classification helps us:

• Identify objects in 3D environments

  
  

• Enable robots to navigate safely

  
  

• Map cities for urban planning

  
  

• Improve augmented reality experiences

Demonstration of point cloud classification using the Flai web application tool. In this example, the Aerial Mapping Classifier was applied to a 3D point cloud. The classifier automatically labeled individual points based on their semantic class—such as buildings, vegetation, roads, and other surface types—directly within the browser-based interface.

🤖 Enter Machine Learning!

Machine learning is like teaching a computer to recognize patterns. When applied to point clouds, ML models learn from examples to say: “Hey, this cluster of points looks like a building!”

There are three main steps:

1. Data Preparation

   
   

2. Model Training

   
   

3. Prediction (Inference)

   
   

4. Validation

   
   

Frameworks like Flai make these steps easier by offering pre-built models, training pipelines, and data utilities tailored for point cloud tasks. Whether you're just starting out or looking to scale up your experiments, Flai helps accelerate development and makes working with 3D data much more accessible.

📂 Step 1: Preparing the Data

Before feeding data into a model, we need to get it ready:

1. Collect: Use sensors like LiDAR to collect raw point clouds.

2. Clean: Remove noise and outliers.

3. Label: Human experts (or clever algorithms) label parts of the cloud. For example: "These points are a car."

4. Split: Divide into training, validation, and test sets.

   
   

🏋️ Step 2: Training the Model

Now the magic begins! We feed the labeled data into a machine learning model. Some popular types of models for point clouds include:

• PointNet/PointNet++: Treats each point independently, then learns global features.

• DGCNN (Dynamic Graph CNN): Builds graphs between nearby points and learns from the connections.

• 3D CNNs: Convert point clouds into voxel grids (think 3D pixels) and apply convolutional neural networks.

🧹 Step 3: Making Predictions

Once trained, the model can take a new point cloud and say, "That’s a tree! That’s a person! That’s a building!"

Point cloud classification display.

✅ Step 4: Validating Your Results

Once you've trained your model and it's making predictions, how do you know it's doing a good job? That's where validation comes in. We use a portion of our labeled dataset (the validation set) to measure how well the model is performing.

Common evaluation metrics include:

• Accuracy: What percentage of points were classified correctly?

  
  

• Precision & Recall: How good is the model at identifying a specific class without being fooled by others?

  
  

• Intersection over Union (IoU): Especially useful for segmentation tasks, it compares the predicted region to the ground truth.

  
  

By validating your results, you ensure that your model isn't just memorizing data (overfitting), but actually learning to generalize.

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Point cloud classification is an exciting intersection of 3D geometry and artificial intelligence. Thanks to modern frameworks like Flai, what once required complex setup and deep domain knowledge is now much more accessible to anyone interested in machine learning. Flai provides a robust foundation with built-in models, tools for data handling, training workflows, and evaluation metrics—all designed for working with 3D point clouds.