The Design Mechanism of 3D Point Cloud Segmentation Explained

3D point cloud segmentation entails the process of categorizing point clouds into several homogeneous areas. But, with points located in the same area having similar properties, the segmentation can get tough due to uneven density of sampling, high redundancy, and the absence of a definite structure of point cloud data. However, this approach has varied applications in robotics including autonomous vehicles, remote sensing, intelligent mapping, and computer vision. In computer vision, point clouds can be captured with four techniques:

• Light Detection and Ranging (LiDAR) systems
• Image-derived Point Cloud
• Synthetic Aperture Radar (SAR) systems
• Red, Green, Blue — Depth (RGB-D) cameras

Due to the differences in survey standards and platforms, their application and data features vary drastically. Let’s examine different approaches and techniques to segment 3D point clouds.

Methods for 3D Point Cloud Segmentation

They are typically categorized into 5 different classes which are as follows:

Edge-based methods

Edges describe the features of the objects. This method detects the boundaries of different regions in the point clouds to generate segmented areas. The standard of these methods is to identify points with a fast change in intensity. But while edge-based methods facilitate fast segmentation, they are not without accuracy issues as they are highly sensitive to the uneven density of point clouds and noise — situations that are common to point cloud data segmentation.

Graph-based methods

These methods consider the point clouds in the form of a graph. Each vertex corresponds to one point in the data while the edges connect to specific pairs of adjacent points. Graph-based methods are precise, efficient, and preferred for robotic applications. This method can also be run interactively or automated with a UI, provided you have prior knowledge of the location of the objects to be segmented.

Attributes-based methods

They are reliable approaches based on the clustering of point cloud data. Clustering methods offer agility in accommodating attributes and spatial relations to integrate different cues into the segmentation. The limitation of these approaches is that they’re heavily dependent on the quality of attributes derived.

This technique adapts to the framework of fuzzy algorithms typically used in combination with cluster merging. The result of this approach is promising but it is also dependent on the choice of parameters and it is tedious and time-consuming.

Region-based methods

It employs neighborhood data to combine adjacent points with the same properties to obtain isolated areas. Consequently, they detect the dissimilarity between different areas. These methods are relatively more accurate than edge-based methods. But, their downside is over or under segmentation as they cannot determine region borders precisely.

Model-based methods

This makes the use of geometric shapes for clustering different points. Points with similar mathematical features are grouped in the same segment. Based on purely mathematical principles, model-based methods are faster and more reliable with outliers. However, the main drawback of these methods is that they lack accuracy when tackling different point cloud sources.

These five methods are classified based on their design mechanisms. However, they follow only two fundamental approaches.

1. It employs purely geometric reasoning techniques along with reliable estimators to fit both linear and nonlinear models to point cloud data. It has a faster running time and gives good results in simple use cases.
2. This approach generates 3D features from point cloud data by leveraging machine learning techniques and feature descriptors. It learns from different types of objects and then uses the final results to categorize the acquired data. Ideal for complex applications, this approach gives reliable results, but it is usually slow and dependent on the results obtained from the feature extraction process.

But, the problem is that most engineers don’t realize what to do after all the data is generated. It is not practical for most construction applications. It needs to be first converted into a 3D model in CAD.

How Point Cloud Modeling Services Can Help

Once you have collected all the point cloud data of the site, you need to compile all of it digitally and transform it into the mesh. When all the spaces between the points are filled, you get a realistic surface. Now you can export the model to the software. This surface reconstruction is called point cloud modeling.

Many companies offer services where they take all their raw data and transform it into accurate and compliant 3D models. They have a team of licensed 3D technicians, engineers, and surveyors for processing raw data and generating precise point cloud models in any format for projects of every size and scope.