ABSTRACT:The automatic reconstruction of 3D building models from airborne point cloud data is still an ongoing research topic. Especially for complex roof shapes, the identification of sub-shapes, the generation of roof boundaries and the construction to well-shaped and topologically correct models remains only partially solved. In this paper, a 3D building reconstruction methodology that is based on the notion of sub-surface growing as a means for point cloud segmentation of planar surfaces is introduced. In contrast to conventional surface growing, the segmentation process continues below other surfaces. As a result, the segments grow larger, their number decrease and the adjacency relations between them become more distinct, thus allowing stricter rules to help identify and differentiate between the root types of the roof sub-shapes and their composition to complex roof structures. In conjunction with a constructive solid modeling approach, the model construction is significantly simplified, as the generation of primitives from subsurface segments is straightforward and their combination to complex shapes can be much easier derived from their interrelations. In the second part of the paper, a boundary generalization approach is presented that allows generating building and segment outlines with regularized shapes from given point sets. Together with sub-surface growing, its usage in the reconstruction of flat roof office buildings is shown. The models are constructed in layers in a bottom-up fashion, each one being the result of a flat sub-surface segment with a generalized boundary, where the regularization rules of one layer are propagated to the next in order to gain wellshaped buildings. A discussion on the so far achieved results and future developments concludes the paper.

Machine learning methods have gained in importance through the latest development of artificial intelligence and computer hardware. Particularly approaches based on deep learning have shown that they are able to provide state-of-the-art results for various tasks. However, the direct application of deep learning methods to improve the results of 3D building reconstruction is often not possible due, for example, to the lack of suitable training data. To address this issue, we present RoofN3D which provides a new 3D point cloud training dataset that can be used to train machine learning models for different tasks in the context of 3D building reconstruction. It can be used, among others, to train semantic segmentation networks or to learn the structure of buildings and the geometric model construction. Further details about RoofN3D and the developed data preparation framework, which enables the automatic derivation of training data, are described in this paper. Furthermore, we provide an overview of other available 3D point cloud training data and approaches from current literature in which solutions for the application of deep learning to unstructured and not gridded 3D point cloud data are presented.

ABSTRACT:With the exception of pure data-driven methods, reconstruction approaches for 3D building models from aerial point clouds often incorporate some level of model information to construct regularized and well-formed roof structures. In the proposed feature-driven approach, low-level roof features like ridge lines, gable and (half-) hip ends, intersecting ridge lines, dormers etc. are detected in a hierarchical rule-based feature recognition process based on a sub-surface segmentation of the point cloud. With each feature type, a number of planar half-spaces are associated that define a certain part of the resulting 3D building model as a mathematical inequality equation. The Boolean intersection of half-spaces define convex building components that can be united to generate more complex building shapes, where the features enforce that the half-spaces are nicely aligned.

A city is a dynamic entity, which environment is continuously changing over time. Accordingly, its virtual city models also need to be regularly updated to support accurate model-based decisions for various applications, including urban planning, emergency response and autonomous navigation. A concept of continuous city modeling is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. A first critical step for continuous city modeling is to coherently register remotely sensed data taken at different epochs with existing building models. This paper presents a new model-to-image registration method using a context-based geometric hashing (CGH) method to align a single image with existing 3D building models. This model-to-image registration process consists of three steps: (1) feature extraction; (2) similarity measure; and matching, and (3) estimating exterior orientation parameters (EOPs) of a single image. For feature extraction, we propose two types of matching cues: edged corner features representing the saliency of building corner points with associated edges, and contextual relations among the edged corner features within an individual roof. A set of matched corners are found with given proximity measure through geometric hashing, and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on collinearity equations. The result shows that acceptable accuracy of EOPs of a single image can be achievable using the proposed registration approach as an alternative to a labor-intensive manual registration process.

ABSTRACT:The automatic reconstruction of 3D building models with complex roof shapes is still an active area of research. In this paper we present a novel approach for local and global regularization rules that integrate building knowledge to improve both the shape of the reconstructed building models and their accuracy. These rules are defined for the planar half-space representation of our models and emphasize the presence of symmetries, co-planarity, parallelism, and orthogonality. By not adjusting building features separately (e.g. ridges, eaves, etc.) we are able to handle more than one feature at a time without considering dependencies between different features. Additionally, we present a new method for reconstructing buildings with concave outlines using half-spaces that avoids the need to partition the models into smaller convex parts. We present both extensions in the context of a fully automatic feature-driven 3D building reconstruction approach where the whole process is suited for processing large urban areas with complex building roofs.