Bootstrapped CNNs for Building Segmentation on RGB-D Aerial Imagery

Detection of buildings and other objects from aerial images has various applications in urban planning and map making. Automated building detection from aerial imagery is a challenging task, as it is prone to varying lighting conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are robust against some of these variations, although they fail to distinguish easy and difficult examples. We train a detection algorithm from RGB-D images to obtain a segmented mask by using the CNN architecture DenseNet.First, we improve the performance of the model by applying a statistical re-sampling technique called Bootstrapping and demonstrate that more informative examples are retained. Second, the proposed method outperforms the non-bootstrapped version by utilizing only one-sixth of the original training data and it obtains a precision-recall break-even of 95.10% on our aerial imagery dataset.Comment: Published at ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Science

Three-dimensional Graphics and Realism—Visible line/surface algorithms

The demand for large geometric models is increasing, especially of urban environments. This has resulted in production of massive point cloud data from images or LiDAR. Visualization and further processing generally require a detailed, yet concise representation of the scene’s surfaces. Related work generally either approximates the data with the risk of over-smoothing, or interpolates the data with excessive detail. Many surfaces in urban scenes can be modeled more concisely by planar approximations. We present a method that combines these polygons into a watertight model. The polygon-based shape is closed with free-form meshes based on visibility information. To achieve this, we divide 3-space into inside and outside volumes by combining a constrained Delaunay tetrahedralization with a graph-cut. We compare our method with related work on several large urban LiDAR data sets. We construct similar shapes with a third fewer triangles to model the scenes. Additionally, our results are more visually pleasing and closer to a human modeler’s description of urban scenes using simple boxes

Change detection in cadastral 3D models and point clouds and its use for improved texturing

By combining terrestrial panorama images and aerial imagery, or using LiDAR, large 3D point clouds can be generated for 3D city modeling. We describe an algorithm for change detection in point clouds, including three new contributions: change detection for LOD2 models compared to 3D point clouds, the application of detected changes for creating extended and textured LOD2 models, and change detection between point clouds of different years. Overall, LOD2 model-to-point-cloud changes are reliably found in practice, and the algorithm achieves a precision of 0.955 and recall of 0.983 on a synthetic dataset. Despite not having a watertight model, texturing results are visually promising, improving over directly textured LOD2 models