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At the core of 3D modeling is the identification of building components and the subsequent organization and mapping of information into the model. In this sense, architectural drawing is quite different from mechanical drawing— it is both a real projection of 3D objects and a semantic symbol used by designers to convey design intent. The advent of AI has also influenced the medical field to usher in new innovation. The development of computer vision and voice technology has also led to changes in everyday modern life.
Extant research has studied 3D reconstruction of architectural structure scenes. Scholars have introduced a new algorithm for 3D reconstruction of single visual density using two perspective boxes (Suryansh et al., 2019). A considerable number of real-time 3D reconstruction problems have been noted with the use of mobile devices, all of which need to be solved using indoor applications. These issues include navigation, augmented reality, and building scanning (Dryanovski, 2017). Existing 3D reconstruction methods for transparent objects are also found unsuitable for the reconstruction configuration of room size (Zhang, 2017), with some finding 3D modeling of the indoor environment playing an important role in various applications such as indoor navigation, building information modeling, and interactive visualization (Cui, 2019). The framework also provides an end-to-end automatic processing method for mapping the initial point cloud to the specified classification results (Zhang & Liang, 2017). The precision 3D measurement system has been thriving because of the research conducted in recent years. Most laser scanners are based on laser scanning technology that can directly obtain 3D data in real time (Sung & Lin, 2017). Depth learning has also been used to reconstruct the 3D model of structural perceptual semantics of cable-stayed bridges. The traditional method of reconstructing the bridge semantic 3D model is usually unable to reconstruct the structure-aware semantic 3D model when using low-quality point clouds (Hu, 2021). The studies have achieved satisfactory results but nonetheless present some problems despite continuous technology updates.
The application of CIR in 3D reconstruction of building structure scenes has been analyzed at different levels by many scholars. Research has systematically discussed and studied the integrity and efficiency of urban outdoor building scene reconstruction (Rebecq, 2018). Damage identification has been conducted in complex scenes and structural surfaces by combining 3D reconstruction technology and digital processing (Fan, 2019). Automatic and semi-automatic reconstruction technology has also been studied based on multiple images by applying the principle of multiple view geometry in computer vision, subsequently applying clustering in pattern recognition and correlation in text retrieval to reconstruction. This process includes fast similarity calculation, visualization, and semi-automatic reconstruction (Bittner, 2018). The recent report on building renovation also posits that there is still work to be done to fully address the issue of sustainability. From the architectural transformation perspective, goals of comprehensive sustainability thus become salient points for discussion (Kamari et al., 2017). A single image stereo reconstruction algorithm based on structured scenes for buildings that cannot be reconstructed by lasers or multiple images has been re-created by existing studies (Han et al., 2019). Applications for military, civilian, and other sectors have been explored, which shows that demand for 3D reconstruction is increasing, and the research on digital models is an important application (Di Ludovico, 2017). These studies show that applying CIR has a positive effect; although, some problems still remain.