Classification of Urban Point Clouds: A Robust Supervised Approach with Automatically Generating Training Data

To reduce the cost of manually annotating training data for parsing outdoor scenes, we propose a supervised approach with automatically generating training data for classifying 3-D point clouds of large-scale urban scenes. In this approach, the input point cloud is aggregated into point clusters, and the disjoint set union issue is combined with geometric attributes of each point cluster to obtain object segments. The prior knowledge among different classes is used to label the segments by using the decision-tree model. Then, the initialized training samples are generated automatically. The confidence estimation for the labeling is employed to filter the mislabeled training samples. With the generated training data, we train a random forest classifier to create the initial classification of the 3-D scene on the set of descriptors for each 3-D point. The classification results are further optimized by multilabel conditional Random Fields. Experimental results on five urban point clouds captured by different types of scanners (i.e., terrestrial laser scanning, vehicle laser scanning, and airborne laser scanning datasets) demonstrate that the proposed approach achieves a competitive classification performance.