Consistency grammar for 3D indoor model checking

Abstract

Indoor 3D models are digital representations of building interiors reconstructed from scanned data acquired by laser scanners, digital depth (RGBD) cameras, and CAD drawings. Consequently, there is noise in the source data and a notable variety in the methods used to treat the noise and to process these data into reconstructed models. Alas, the correctness of these reconstructions and thus their suitability for a given application are uncertain. There is a lack of a robust base logic that would allow for controlling the consistency of these (automatically) generated models. Fortunately, correctness criteria are well‐defined through existing international standards. Hence, we propose a conceptual framework based on formal grammars to check the semantic, geometric, and topological consistency of a reconstructed 3D model. The proposed method proceeds in three steps to validate the model: (1) correctness checking of individual components; (2) consistency verification of instances’ interactions; and (3) model consistency check for targeted applications. Our method identifies the components in the model that violate the given rules derived from the current standards and expert knowledge. Ultimately, we propose a quantified formulation of our method that may be straightforwardly integrated into industrial‐level model checkers. The approach is independent of level of details and reconstruction method.

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

“…Another interesting outcome of our experiment is that rooms without an opening or the missing doors can be flagged during the evacuation scenario and the model can be returned to the reconstruction step for further corrections. This is similar to solution suggested by [Nikoohemat et al, 2020a] for consistency check of 3D models from point clouds. However, this can not be checked for missing walls or mislabeled walls.…”

“…The works closest to ours consist of (1) indoor reconstruction methods that form a boundary representation or standardized BIM models (e.g. IFC) [Nikoohemat et al, 2020a] and of (2) (commercial) agent-based simulation methods that use the standardized BIM models as simulation sandboxes, e.g. [Sun and Turkan, 2020].…”

New steps against scientific gerrymandering: A new operational definition of geographic information science

“…GISc has formal Grammars or Syntax, offering rules for joining symbols. As an example, the languages of GIS can be cited, or at some more abstract level of formalization of spatial relationships and connectivity (Liu et al, 2018), modeling of natural bodies (Rongier et al, 2017), identification of shapes (Li et al, 2019), consistency verification in 3D models (Nikoohemat et al, 2021), and the classics on image syntax (Dondis, 1973), among others.…”

Further Steps against Scientific Gerrymandering: A New Definition of Geographic Information Science

“…GISc has formal Grammars or Syntax, offering rules for joining symbols. As an example, the languages of GIS can be cited, or at some more abstract level of formalization of spatial relationships and connectivity [29], modeling of natural bodies [30], identification of shapes [31], consistency verification in 3D models [32], and the classics on image syntax [33], among others.…”