Towards the Automatic 3d Parametrization of Non-Planar Surfaces From Point Clouds in Hbim Applications

Abstract

3D laser scanning and photogrammetric 3D reconstruction generate point clouds from which the geometry of BIM models can be created. However, a few methods do this automatically for concrete architectural elements, but in no case for the entirety of heritage assets. A novel procedure for the automatic recognition and parametrization of non-planar surfaces of heritage immovable assets is presented using point clouds as raw input data. The methodology is able to detect the most relevant architectural features in a point cloud and their interdependences through the analysis of the intersections of related elements. The non-planar surfaces detected, mainly cylinders, are studied in relation to the neighbouring planar surfaces present in the cloud so that the boundaries of both the planar and the non-planar surfaces are accurately defined. The procedure is applied to the emblematic Castle of Torrelobatón, located in Valladolid (Spain) to allow the cataloguing of required elements, as illustrative example of the European defensive architecture from the Middle age to the Renaissance period. Results and conclusions are reported to evaluate the performance of this approach.

The Application of Historic Building Information Modelling (HBIM) to Cultural Heritage: A Review

“…Model creation-Arguably the next evolution of point cloud segmentation is where a modelling algorithm detects areas of the point cloud belonging to a single object, separates the point and then creates a 3D object in that place. This method has been investigated and applied with some success to individual work [96], but there is yet to be a fully automated process [97]. Other works have repeatedly concluded that modelling algorithms are not yet developed enough to model complex architecture or differences due to aging [73,74,[98][99][100].…”

“…[45] suggests that modelling requires a degree of technical insight regarding constructive techniques which cannot be automatically determined. The most successful automatic modelling techniques model geometric primitives [97,101]. Of these, the accuracy is potentially insufficient for certain use cases, with [97] only achieving a distance accuracy for columns of approximately 64 mm.…”

“…The most successful automatic modelling techniques model geometric primitives [97,101]. Of these, the accuracy is potentially insufficient for certain use cases, with [97] only achieving a distance accuracy for columns of approximately 64 mm.…”