Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review

Abstract

Point cloud processing is an essential task in many applications in the AEC domain, such as automated progress assessment, quality control and 3D reconstruction. As much of the procedure used to process the point clouds is shared among these applications, we identify common processing steps and analyse relevant algorithms found in the literature published in the last 5 years. We start by describing current efforts on both progress and quality monitoring and their particular requirements. Then, in the context of those applications, we dive into the specific procedures related to processing point clouds acquired using laser scanners. An emphasis is given to the scan planning process, as it can greatly influence the data collection process and the quality of the data. The data collection phase is discussed, focusing on point cloud data acquired by laser scanning. Its operating mode is explained and the factors that influence its performance are detailed. Data preprocessing methodologies are presented, aiming to introduce techniques used in the literature to, among other aspects, increase the registration performance by identifying and removing redundant data. Geometry extraction techniques are described, concerning both interior and outdoor reconstruction, as well as currently used relationship representation structures. In the end, we identify certain gaps in the literature that may constitute interesting topics for future research. Based on this review, it is evident that a key limitation associated with both Scan-to-BIM and Scan-vs-BIM algorithms is handling missing data due to occlusion, which can be reduced by multi-platform sensor fusion and efficient scan planning. Another limitation is the lack of consideration for laser scanner performance characteristics when planning the scanning operation and the apparent disconnection between the planning and data collection stages. Furthermore, the lack of representative benchmark datasets is hindering proper comparison of Scan-to-BIM and Scan-vs-BIM techniques, as well as the integration of state-of-the-art deep-learning methods that can give a positive contribution in scene interpretation and modelling.

Machine and Deep Learning Implementations for Heritage Building Information Modelling: A Critical Review of Theoretical and Applied Research

“…The semi-systematic review helps to identify theoretical approaches and gaps in the literature and verifies the progress of publications over time [19].Despite previous publications of similar literature review such as [20] or [21] our work bring novelty with its increased number of papers sampled, attention on the achieved results and in-depth analysis of publications. It proved an adequate research strategy as the research questions involve a theme explored by diverse disciplines.…”

Scan to BIM: Arquitetura e instalações MEP

“…O laserscanning foi uma mais-valia na precisão e perceção para a modelação em ARCHICAD [3]. O programa utilizado apresenta uma enorme versatilidade, não só como ferramenta de modelação na arquitetura, como também nas especialidades MEP.…”

“…Embora a nuvem de pontos seja bastante precisa, este local em caso de estudo é um espaço pouco iluminado, cheio de tubagens e equipamentos, visualmente muito intenso e cheio de pormenores. Por essa razão, foi imprescindível adquirir uma nuvem de pontos com maior resolução e, por consequência, originando ficheiros bastante pesados [3].…”

“…Uma perspetiva relevante que pode ser vista como uma desvantagem, é o desgaste apresentado no equipamento, nomeadamente o sobreaquecimento dos processadores e/ou placas gráficas, havendo necessidade de utilizar placas gráficas com grande capacidade devido ao grande volume de dados dos ficheiros [3,4,9]. Outra desvantagem identificada é o facto de não existir forma de carregar as nuvens de pontos num ficheiro IFC, que seria muito útil em fases intermédias de desenvolvimento do projeto, para visualização e partilha.…”