Change detection – knowledge about the objects in a scene allows for them to be compared to design models or as-builts.Once you can extract this information about what a 3D scene contains, then many downstream processes can be unlocked for value-added automation. The two terms are often used interchangeably however a general definition of each term usually separates them as: segmentation being the action of separating the point cloud into different objects while classification applies the understanding of what those objects represent.Ĭlassification is a key part of automating the understanding of point clouds for various purposes. Point Cloud Classification & Segmentation
This automates the contextualisation of data, making it far easier and more efficient to access the information required - opening new possibilities for cost-effective applications of static, mobile scanning and other reality capture techniques and technologies. Applying object recognition to point clouds data classification is a powerful first step towards harnessing point cloud data for more effective usage.Ī big trend impacting the utility of reality capture data is the application of Artificial Intelligence/Machine Learning (AI/ML) algorithms to detect and extract objects and features from point clouds data sets - a process of scan data classification that products like Vercator are at the forefront of. Point clouds contain huge amounts of spatial information, but unless that information is processed and labelled, it can’t be effectively used or even understood.
0 kommentar(er)
