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.
Highlights
The diagnostic step leads to a list of corrupted nodes CN odes and their attributes that will be corrected in a subsequently step leading to a list CorrN odes together with the new nodes
The corrected attributes that affect other parse tree nodes are considered, the according changes are propagated based on the semantic rules of weighted attribute context-free grammar (WACFG)
That would lead to conflicts between the current tree structure and the underlying grammar rules
Summary
Formal grammars demand a mechanism that fits data to underlying grammar rules. For this issue, several parse algorithms have been introduced. Han and Zhu (2009) used a two-staged approach in order to parse images based on a graph grammar. Han and Zhu (2009) used a two-staged approach in order to parse images based on a graph grammar They assume that man-made objects are modelled by sub-objects using the grammar rules. Schmittwilken (2012) presented an estimator and parser for the identification and interpretation of facades from LIDAR point clouds based on attribute grammars and a-priori probabilities. All these approaches expect offline data in order to parse the underlying grammar rules. The identification of facade parts is explained in subsection 3.1 while the algorithms for the incorporation of these identified parts based on the incre-
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