Reconstruction Of Building Objects Research Articles

INCREMENTAL REFINEMENT OF FAÇADE MODELS WITH ATTRIBUTE GRAMMAR FROM 3D POINT CLOUDS

Data acquisition using unmanned aerial vehicles (UAVs) has gotten more and more attention over the last years. Especially in the field of building reconstruction the incremental interpretation of such data is a demanding task. In this context formal grammars play an important role for the top-down identification and reconstruction of building objects. Up to now, the available approaches expect offline data in order to parse an a-priori known grammar. For mapping on demand an on the fly reconstruction based on UAV data is required. An incremental interpretation of the data stream is inevitable. This paper presents an incremental parser of grammar rules for an automatic 3D building reconstruction. The parser enables a model refinement based on new observations with respect to a weighted attribute context-free grammar (WACFG). The falsification or rejection of hypotheses is supported as well. The parser can deal with and adapt available parse trees acquired from previous interpretations or predictions. Parse trees derived so far are updated in an iterative way using transformation rules. A diagnostic step searches for mismatches between current and new nodes. Prior knowledge on fac¸ades is incorporated. It is given by probability densities as well as architectural patterns. Since we cannot always assume normal distributions, the derivation of location and shape parameters of building objects is based on a kernel density estimation (KDE). While the level of detail is continuously improved, the geometrical, semantic and topological consistency is ensured.

INCREMENTAL REFINEMENT OF FAÇADE MODELS WITH ATTRIBUTE GRAMMAR FROM 3D POINT CLOUDS

Abstract. Data acquisition using unmanned aerial vehicles (UAVs) has gotten more and more attention over the last years. Especially in the field of building reconstruction the incremental interpretation of such data is a demanding task. In this context formal grammars play an important role for the top-down identification and reconstruction of building objects. Up to now, the available approaches expect offline data in order to parse an a-priori known grammar. For mapping on demand an on the fly reconstruction based on UAV data is required. An incremental interpretation of the data stream is inevitable. This paper presents an incremental parser of grammar rules for an automatic 3D building reconstruction. The parser enables a model refinement based on new observations with respect to a weighted attribute context-free grammar (WACFG). The falsification or rejection of hypotheses is supported as well. The parser can deal with and adapt available parse trees acquired from previous interpretations or predictions. Parse trees derived so far are updated in an iterative way using transformation rules. A diagnostic step searches for mismatches between current and new nodes. Prior knowledge on fac¸ades is incorporated. It is given by probability densities as well as architectural patterns. Since we cannot always assume normal distributions, the derivation of location and shape parameters of building objects is based on a kernel density estimation (KDE). While the level of detail is continuously improved, the geometrical, semantic and topological consistency is ensured.

Automatic Reconstruction of Building Objects From Multiaspect Meter-Resolution SAR Images

Reconstruction of 3-D objects from multiaspect high- resolution synthetic aperture radar (SAR) images is of great importance for SAR technology applications. In this paper, simple building objects are modeled as cuboids, and an approach for automatic reconstruction of 3-D building objects from multiaspect SAR images in meter resolution is developed. The edge detector of constant false alarm rate and a Hough transform technique for parallel line segment pairs are first employed to extract the parallelogram-like image of the building walls in SAR images. A set of probability density functions is presented to describe the object images and their multiaspect coherence. The maximum-likelihood estimation of an object is then derived from its multiaspect object images. A hybrid priority criterion is defined to evaluate the reliability of the reconstruction result. An automatic reconstruction algorithm is further developed to match object images of different aspects and, finally, to reconstruct the building objects. Besides, an iterative method is proposed for the coregistration of multiaspect building images. Four-aspect simulated images of a virtual scene and four-aspect Pi-SAR images over the campus of Tohoku University, Japan, are investigated. Reconstruction of building objects from their multiaspect images shows the fidelity of the whole process chain and the feasibility of 3-D objects automatic reconstruction from multiaspect SAR images. At last, a practical application that is based on spaceborne meter-resolution SAR is proposed.