Martin Kada

Aggregation of 3D Buildings Using a Hybrid Data Approach

With the increasing availability of large-area 3D city models, there is also a rising need for cartographic generalization algorithms to prepare this data so as to improve both the performance and the perceivability of their visualizations, especially in a mobile-mapping context. Most research has focused on the geometric simplification of single 3D buildings, which can only reach a certain degree of abstraction until building-specific geometric constraints are violated. To obtain further levels, an aggregation of building groups is necessary. The previously published building generalization approach based on cell decomposition is therefore extended to use morphological operations on a raster representation of the initially vectorial data. By repeatedly applying dilation and erosion, the space between buildings closes, which boosts the importance of the already available building blocks in these areas so that they are now included in the generalized model. Even though the results are encouraging from a technical point of view, it leads to a fundamental discussion of how the roofs of aggregated 3D buildings should appear.

Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing

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.

Smooth transformations between generalized 3D building models for visualization purposes

The generation of highly detailed 3D city models has significantly matured over the last couple of years. Especially for visualization purposes, these models are needed at several levels of detail (LOD) in fine granular steps that also allow for a smooth transformation from one level to the next to avoid pop-up artifacts. Several methods have been proposed for the automatic generalization of 3D building models that simplify the geometry of given boundary-based models at discrete LODs. In this paper, we present an approach to generate a transformation sequence between two discrete LODs with the purpose to later build a continuous LOD sequence. The method is based on mesh simplification techniques that are extended to a restricted use of edge collapse and vertex move operations applied on triangle meshes augmented by additional breaking lines. The validity of the approach is demonstrated on a case study that is close to reality.