Image-based 3D reconstruction for Multi-Scale civil and infrastructure Projects: A review from 2012 to 2022 with new perspective from deep learning methods

Lu, Yujie; Wang, Shuo; Fan, Sensen; Lu, Jiahui; Li, Peixian; Tang, Pingbo

doi:10.1016/j.aei.2023.102268

Cited by 9 publications

(2 citation statements)

References 101 publications

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“…Relevant studies shown that street view image could represent the objective environment in the city (Tao et al 2022;Aikoh et al 2023). At present, it is widely used in the researches such as mental health (Wang et al 2022b, Yuan et al 2023, perceptual evaluation (Lyu et al 2022), walkability (Li et al 2022, Jeon and, street visual quality, and 3D city reconstruction (Lu et al 2024). With the development of computer vision technique, semantic segmentation improved the high efficiency of processing massive data.…”

Section: Introductionmentioning

confidence: 99%

Measuring human perception of residential built environment through street view image and deep learning

Meng,

Sun,

Lyu

et al. 2024

Environ. Res. Commun.

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As an important part of the urban built environment, streets exploring the influence mechanism between the built environment and human perception. It is one of the issues in building healthy cities. In this study, the residential streets of Zhongshan Distict, Dalian were selected as the study site, including Mountain Low-rise Neighborhood, Old Mid-rise Neighborhood, and Modern High-rise Neighborhood. Meanwhile, spatial measurement and human perception perception evaluation of the street environment were based on Deep learning and street view image (SVI). The study used human perceptions as dependent variables, and physical features as the independent variables. Finally, two regression models of positive and negative perceptions were established to analyze the relationship between them. The results showed that in the three types of neighborhood, positive perception was mainly focused on Mountain Low-rise Neighborhood; Negative perception was mainly focused on Old Mid-rise Neighborhood. Greenness, Openness, Natural Landscape, Natural to artificial ratio of the horizontal interface, and Natural to artificial ratio of the vertical interface had a positive influence on positive perception. Pedestrian occurrence rate, Enclosure, and Vehicle Occurrence rate had a negative influence on negative emotive. Greenness was the physical feature that most affected human perception. This study provided a method for objectively evaluating the quality of the street built environment. It is important for promoting the quality of residential streets and public mental health.

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Section: Introductionmentioning

confidence: 99%

Measuring human perception of residential built environment through street view image and deep learning

Meng,

Sun,

Lyu

et al. 2024

Environ. Res. Commun.

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show abstract

“…With the rapid development of computer vision and image processing technologies, 3D image reconstruction has become an important research field, widely applied in medical imaging, industrial inspection, virtual reality, and other fields [1][2][3][4]. In medical imaging, 3D image reconstruction technology can provide more intuitive and precise internal structure visualization, playing an important auxiliary role in the diagnosis and treatment of diseases [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Application of Multi-objective Optimization in 3D Image Reconstruction

Zhang

2024

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Reconstructing as-built beam bridge geometry from construction drawings using deep learning-based symbol pose estimation

Faltin,

Schönfelder,

Gann

et al. 2024

Advanced Engineering Informatics

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Image-based 3D reconstruction for Multi-Scale civil and infrastructure Projects: A review from 2012 to 2022 with new perspective from deep learning methods

Cited by 9 publications

References 101 publications

Measuring human perception of residential built environment through street view image and deep learning

Measuring human perception of residential built environment through street view image and deep learning

Application of Multi-objective Optimization in 3D Image Reconstruction

Reconstructing as-built beam bridge geometry from construction drawings using deep learning-based symbol pose estimation

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