A Semiautomatic Large-Scale Detection of Simple Geometric Primitives for Detecting Structural Defects from Range-Based Information

Abstract

Buildings in Cultural Heritage environments exhibit some common structural defects in elements which can be recognized by their differences with respect to the ideal geometric model. The global approach consists of detecting misalignments between elements corresponding to sections perpendicular to an axis, e.g. The local approach consists of detecting lack of verticality or meaningful differences (facades or internal walls) in curved elements with typical components (apses or vaults, e.g.) appearing in indoor environments. Geometric aspects concern to the basic model which supports successive layers corresponding to materials analysis and mechanical structural behaviour. A common strategy for detecting simple shapes consists of constructing maps of normal which can be extracted by an appropriate sampling of unit normal vectors linked to a points cloud. The most difficult issue concerns to the sampling process. A profusion of decorative details or even the small variations corresponding to small columns which are prolonging the nerves of vaults generate a dispersion of data which can be solved in a manual way by removing notrelevant zones for structural analysis. This method can be appropriate for small churches with a low number of vaults, but it appears as tedious when we are trying to analyse a large cathedral or an urban district. To tackle this problem different strategies for sampling information are designed, where some of them involving geometric aspects have been implemented. We illustrate our approach with several examples concerning to outdoor urban districts and indoor structural elements which display different kinds of pathologies.