3D Computer Vision with AWS Point Cloud Library (PCL) on EC2

In rapidly evolving world of computer vision, 3D data processing is becoming more critical than ever. Whether it’s for autonomous vehicles, robotics, or augmented reality, analyzing three‐dimensional point clouds has opened new avenues of research and development.

In this blog, we’ll explore how to harness the power of the Point Cloud Library (PCL)—a robust open‐source toolkit for processing 2D/3D image and point cloud data—using Amazon EC2. We will cover the basics of 3D computer vision, an overview of PCL’s capabilities, setting up an AWS EC2 instance, installing and configuring PCL, and running some sample pipelines to solve real-world problems.

3D Point Cloud Processing and the Point Cloud Library (PCL)

Three-dimensional computer vision is the discipline that deals with processing, analyzing, and interpreting 3D data—often captured by LiDAR, stereo cameras, or depth sensors. The raw data typically comes in the form of point clouds, where each point represents a sample in space with coordinates (x, y, z) and possibly additional attributes like color or intensity. Processing these point clouds to extract useful information requires robust algorithms for tasks such as noise removal, feature extraction, registration, segmentation, and even object recognition. 

The Point Cloud Library (PCL) is a powerful, open-source framework developed to facilitate such tasks. With over a thousand functions spread across modules for filtering, segmentation, feature estimation, registration, and visualization, PCL has become a go-to resource for researchers and practitioners alike.  

Why Use AWS EC2 for 3D Computer Vision? 

Processing large point cloud datasets can be computationally intensive. AWS EC2 offers a flexible, scalable environment where you can choose the instance type that best suits your workload—from high-CPU instances for batch processing to GPU-accelerated machines for more demanding tasks.

Fig. 1.1 AWS EC2 instance Architecture 

 By deploying your PCL-based solutions on EC2, you can: 

• Scale as Needed: Spin up multiple instances to process large datasets in parallel. 

• Access High-Performance Hardware: Use instances equipped with modern CPUs and GPUs to speed up computation. 

• Integrate with Other AWS Services: Easily connect to S3 for storage, leverage AWS CloudFormation for infrastructure automation, or integrate with SageMaker if you later add machine learning capabilities. 

Using EC2 for 3D computer vision projects allows you to develop and test your algorithms in an environment that can grow with your project’s needs. 

Explore how AWS Panorama for edge-based computer vision applications, enabling real-time AI-driven insights and automation at scale. Learn more here

Step-by-Step AWS EC2 Setup for 3D Vision

Choosing the Right Instance 

Select an EC2 instance based on your computational needs: 

• CPU & Memory: Compute-optimized instances (C5) are ideal for CPU-bound tasks like filtering and segmentation. 

• GPU Support: Use GPU-accelerated instances (G4, P3) for visualization and parallel-processing tasks. 

• Storage: Choose Amazon EBS or instance store volumes for handling large point cloud datasets efficiently

Launching and Configuring the Instance 

• Launch an EC2 Instance: Use AWS Console or CLI with an Ubuntu or Amazon Linux AMI. 

• Security Groups: Configure SSH access and required ports for visualization. 

• IAM Role: Attach permissions for S3 and other AWS services if needed in your workflow. 

Installing Dependencies

• Before installing PCL, you’ll need to install several system dependencies. For visualisation, PCL relies on libraries such as Boost, Eigen, FLANN, and VTK. After installing these dependencies, you can proceed with PCL installation. 

PCL Installation and Configuration Guide for EC2

There are several ways to install PCL on a Linux instance on EC2. You can either use the package manager or compile from source. While package manager installations are faster, compiling from source can provide access to the latest features and optimizations. 

Option 1: Installing via Package Manager 

For Ubuntu, simply run: 

sudo apt-get install -y libpcl-dev 

This command installs PCL along with its dependencies. However, note that the version available via apt-get might not be the latest release. 

Option 2: Compiling from Source 

To compile PCL from source: 

Clone the Repository

git clone https://github.com/PointCloudLibrary/pcl.git 
cd pcl 
  

Create a Build Directory 

mkdir build && cd build 
  

Run CMake

cmake ..

You may customize the build options (e.g., disable modules you don’t need) for faster compilation. 

Build and Install: 

make -j$(nproc) 
sudo make install 

Compiling from source ensures you have full control over your build configuration and can optimize the build for your instance’s hardware. 

Implementing 3D Vision Algorithms: Registration to Recognition

Fig 1.2. 3D Computer Vision Algorithms with PCL 

Now that your EC2 instance is ready and PCL is installed, it’s time to use it. Let’s walk through a few key 3D computer vision tasks using PCL. 

1. Point Cloud Filtering

Noise is an inherent issue in point cloud data. PCL offers several filtering algorithms, such as Statistical Outlier Removal and Voxel Grid filtering, to improve the quality of your data. 

• Statistical Outlier Removal - This filter examines the distribution of point-to-neighbour distances and removes points that deviate significantly from the mean. Such filtering is crucial before performing any downstream processing like feature extraction or segmentation. 

2. Feature Extraction and Keypoint Detection

After cleaning your data, the next step is to extract meaningful features and detect key points. These features can later be used for tasks such as object recognition or registration. 

3. Registration and Alignment

One of the key challenges in 3D computer vision is aligning multiple point clouds. Whether you’re stitching together scans from different viewpoints or performing object recognition, registration algorithms like Iterative Closest Point (ICP) are indispensable. This process is vital when combining multiple scans into a coherent 3D model. 

4. Segmentation and Object Detection

Segmentation involves partitioning a point cloud into clusters corresponding to distinct objects or surfaces. PCL provides various segmentation techniques, such as region-growing or model-based segmentation (using RANSAC). 

Segmenting planar regions is especially useful in indoor mapping or autonomous driving applications, where distinguishing the road or walls from obstacles is crucial.