Automatic reconstruction of parametric building models from indoor point clouds

Abstract

a b s t r a c tWe present an automatic approach for the reconstruction of parametric 3D building models from indoor point clouds. While recently developed methods in this domain focus on mere local surface reconstructions which enable e.g. efficient visualization, our approach aims for a volumetric, parametric building model that additionally incorporates contextual information such as global wall connectivity. In contrast to pure surface reconstructions, our representation thereby allows more comprehensive use: first, it enables efficient high-level editing operations in terms of e.g. wall removal or room reshaping which always result in a topologically consistent representation. Second, it enables easy taking of measurements like e.g. determining wall thickness or room areas. These properties render our reconstruction method especially beneficial to architects or engineers for planning renovation or retrofitting. Following the idea of previous approaches, the reconstruction task is cast as a labeling problem which is solved by an energy minimization. This global optimization approach allows for the reconstruction of wall elements shared between rooms while simultaneously maintaining plausible connectivity between all wall elements. An automatic prior segmentation of the point clouds into rooms and outside area filters large-scale outliers and yields priors for the definition of labeling costs for the energy minimization. The reconstructed model is further enriched by detected doors and windows. We demonstrate the applicability and reconstruction power of our new approach on a variety of complex real-world datasets requiring little or no parameter adjustment.

Exploiting Indoor Mobile Laser Scanner Trajectories for Semantic Interpretation of Point Clouds

“…For the detection of permanent structure such as walls, floor and ceiling, we use a method that exploits the topology of planar primitives. Current indoor * Corresponding author reconstruction methods are often limited to Manhattan-World structure (Budroni and Boehm, 2010) or employ horizontal floor/ceiling and vertical wall assumption (Ochmann et al, 2016;Oesau et al, 2014;Xiao and Furukawa, 2014). Other methods generate 2.5D models (Oesau et al, 2014;Turner and Zakhor, 2014) or do not consider the detection of openings and addition of semantics (Mura et al, 2014a;Oesau et al, 2014;Xiao and Furukawa, 2014).…”

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

“…However, the integration of knowledge is still rare, with few example of hybrid pipelines [83,84]. Our proposed approach constitute a hybrid method inspired by previous work in shape recognition [85][86][87][88], region growing pipelines [80,89,90] and abstraction-based segmentation [91][92][93][94][95] relying on 3D connected component labelling and voxel-based segmentation. As such, different features presented in Table 1 constitute the base for segmentation.…”

Point Cloud Classification of Tesserae From Terrestrial Laser Data Combined With Dense Image Matching for Archaeological Information Extraction

“…However, the integration of knowledge is still rare, with few example of hybrid pipelines (Ben Hmida et al, 2012;Pu and Vosselman, 2009). Our proposed approach constitute a hybrid method inspired by previous work in shape recognition (Chaperon and Goulette, 2001;Lin et al, 2013;Ochmann et al, 2016;Schnabel et al, 2007), region growing pipelines (Dimitrov and Golparvar-Fard, 2015;Nurunnabi et al, 2012;Rusu and Blodow, 2009) and abstraction-based segmentation (Aijazi et al, 2013;Douillard and Underwood, 2011;Girardeau-Montaut, 2006;Girardeau-Montaut et al, 2005;Samet and Tamminen, 1988) relying on 3D connected component labelling and voxel-based segmentation. Indeed, unstructured point cloud can benefit of structural properties that can be used as part of a segmentation process.…”