Abstract
Abstract. We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.
Highlights
Extraction of semantic information from point clouds enables us to understand a scene, classify objects and generate high-level models with CAD-like geometries from them
Often the proposed solutions were handcrafted to the application at hand: buildings have been modeled by using common image processing techniques such as edge detection (Haala et al, 1998, Brenner, 2000) or by fitting planes to point clouds (Rusu et al, 2007); road networks have been modeled by handcrafted features and Digital Terrain Models (DTMs) algorithms used heuristics about the size of objects to create a DTM from a Digital Surface Models (DSMs)
We evaluate our approach on challenging aerial photogrammetry point clouds
Summary
Extraction of semantic information from point clouds enables us to understand a scene, classify objects and generate high-level models with CAD-like geometries from them. It can provide a significant improvement to existing algorithms, such as those used to construct Digital Terrain Models (DTMs) from Digital Surface Models (DSMs) (Unger et al, 2009). With the growing popularity of laser scanners, the availability of drones as surveying tools, and the rise of commercial photogrammetry software capable of generating millions of points from images, there exists an increasing need for fully automated extraction of semantic information from this kind of data. We focus here on machine learning techniques, that will allow the users to detect objects categories of their own choice
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