Automatic object detection in point clouds

Classification Approaches and Method Comparison

Point clouds are used as a data source for mapping tasks in various application fields. Before an object can be mapped, it needs to be detected in the point cloud, preferably by automatic means. The development of detection methods is a complex task due to the significant diversity of objects, the random structure of point clouds, and the different characteristics of point clouds created by airborne, mobile or static systems or image matching. This article introduces classification approaches for automatic object detection and highlights several challenges related to the topic.

Automatic object detection in point clouds is done by separating points into different classes in a process referred to as ‘classification’ or ‘filtering’. The types of objects, and thus the classes, depend mainly on the application for which the point cloud was collected. For example, the classes for a power line maintenance project will be different from the classes of a road maintenance or a city mapping project.

This article discusses two classification approaches: point-based and group-based classification. Point-based classification means that the software looks at one point at a time and analyses the attributes of the point, its connection with points in its closest environment or its relationship to a reference element.

For group-based classification, points are first assigned to groups in a process that is sometimes also called ‘segmentation’. Then, the software looks at the group and analyses the group geometry and attributes, the similarity to sample groups, or the relationship to other groups or a reference element. More.

Source: GIM International