Classification of Aerial Photogrammetric 3D Point Clouds
This work addresses the need for efficient and accurate point cloud labeling for environmental modeling and scene understanding, representing an incremental improvement over existing methods.
The paper tackles the problem of semantic classification of aerial photogrammetric 3D point clouds by incorporating color information, achieving high accuracy and processing 10 million points in under 3 minutes on a desktop computer.
We present a powerful method to extract per-point semantic class labels from aerialphotogrammetry data. Labeling 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.