Progressive Model-Driven Approach for 3D Modeling of Indoor Spaces

This paper focuses on the 3D modeling of the interior spaces of buildings. Three-dimensional point clouds from laser scanners can be considered the most widely used data for 3D indoor modeling. Therefore, the walls, ceiling and floor are extracted as the main structural fabric and reconstructed. In this paper, a method is presented to tackle the problems related to the data including obstruction, clutter and noise. This method reconstructs indoor space in a model-driven approach using watertight predefined models. Employing the two-step implementation of this process, the algorithm is able to model non-rectangular spaces with an even number of sides. Afterwards, an improvement process increases the level of details by modeling the intrusion and protrusion of the model. The 3D model is formed by extrusion from 2D to 3D. The proposed model-driven algorithm is evaluated with four benchmark real-world datasets. The efficacy of the proposed method is proved by the range of [77%, 95%], [85%, 97%] and [1.7 cm, 2.4 cm] values of completeness, correctness and geometric accuracy, respectively.

Automated summary

This paper focuses on 3D modeling of interior spaces in buildings using three-dimensional point clouds obtained from laser scanners. The walls, ceiling, and floor are extracted as the main structural fabric and reconstructed. The paper presents a method to address data-related issues such as obstruction, clutter, and noise. By employing a model-driven approach using watertight predefined models, the algorithm is able to effectively reconstruct non-rectangular spaces with an even number of sides. The proposed method is evaluated using real-world datasets, demonstrating its effectiveness in terms of completeness, correctness, and geometric accuracy with values ranging between [77%, 95%], [85%, 97%], and [1.7 cm, 2.4 cm], respectively.