Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

Abstract

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research.

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

“…Zeng [59] applied a deep neural network (DNN) and a set of rules to determine the 2D shape of the house (considering a set of predefined shapes), estimate its size, classify the roof type and estimated height and determine the area occupied by secondary structures (i.e., garage) so arbitrary polygons are fitted to it, optimizing the reconstruction process. Klimkowska [72] presented a complete review on façade reconstruction with an emphasis on building opening detection. The authors indicate the lack of benchmark datasets for different architectural styles as the main reason for the lack of development in this area.…”

Indoor and outdoor multi-source 3D data fusion method for ancient buildings

“…For this aim, the different available metric data sources are systemized and evaluated in terms of their suitability. The paper [19] presented an overview of 3D building façade reconstruction; it focused on highlighting the current research on data and key technologies used to enrich building façades, especially the methods used for façade parsing and building-opening detection. It is a feasible way to minimize the obscuring of façade elements by static and dynamic elements of the urban environment using data fusion from multi-modal sources and different platforms.…”

Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review

“…Different datasets of the railway environment are discussed in [12]. Techniques for point cloud analysis are reviewed in [9] and [13]. However, there seems to be a gap in systematic reviews specifically targeting point cloud segmentation or object detection methods.…”