Procedural 3D reconstruction and quality evaluation of indoor models

Building Information Modelling (BIM) plays an important role in the digital transformation of the construction sector and built environments. BIM promises to achieve better quality infrastructures and to shorten the duration of construction projects, and also has additional values in the global infrastructure market, as it potentially provides more efficiency in collaboration, transparency and information management, and greater intelligence in the decision-making process during the whole lifetime of buildings. In addition, up-to-date 3D building models serve as a versatile data source for various applications such as energy simulation, navigation, location-based services, and emergency response. Today, 3D building models are available only for newly designed or recently constructed buildings. A large proportion of existing buildings have been in existence for many decades. Meanwhile, the 3D models are not often updated to reflect changes in an existing building during the different stages of its lifecycle. Automated methods for efficient and reliable generation of 3D building models have the potential to expand the application domains to existing buildings. Although recent lidar scanning and photogrammetry techniques allow efficient capturing of the as-is condition of the built environment, the development of automated processes for the production of accurate, correct, and complete 3D models from the data remains a challenge. In addition, quantitative measurement of the quality of the 3D reconstructed models is essential, as it enables the measurement of the faithfulness of the models in representing the physical built environment. This thesis aims to develop a novel approach to automated reconstruction of indoor models and to provide a comprehensive method for quantitative evaluation of the quality and change detection of indoor models. The thesis contains three main contributions. First, a shape grammar approach for procedural modelling of indoor environment containing Manhattan world designs from lidar data is proposed. The hypothesis of this research is that understanding and translating the principles of indoor architectural design into a modelling algorithm will provide the capability to overcome the challenges and assist the reconstruction of a 3D semantic-rich model. Second, a procedural method for automated reconstruction of generic indoor models (i.e., Manhattan and non-Manhattan world buildings) using a stochastic approach is developed. The approach is based on a combination of a shape grammar and a data-driven approach, which facilitates the automated application of grammar rules in the production process and enhances its robustness to incomplete and inaccurate input. Third, a comprehensive method for quality evaluation, comparison, and change detection of 3D indoor models is proposed. The evaluation method facilitates a quantitative assessment of geometric quality of indoor models in terms of three quality aspects: completeness, correctness, and accuracy. The change detection method enables identification of redundant elements in existing 3D models and the missing elements in indoor environments. A series of experiments was carried out to evaluate the performance of the proposed methods on synthetic and real datasets, and the results show the capability of the methods for the reconstruction of complex indoor environments with high accuracy, completeness, and correctness.