IMGTR: Image-triangle based multi-view 3D reconstruction for urban scenes

Hu, Zhihua; Hou, Yaolin; Tao, Pengjie; Shan, Jie

doi:10.1016/j.isprsjprs.2021.09.009

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…In research that focuses specifically on semantic segmentation, the extraction of buildings and their elements remains a problem of machine learning techniques and only becomes an architectural question if it is forming part of a larger research process. This includes research on architectural challenges at a scale and complexity difficult to complete using manual methods, such as extracting roof or façade textures to increase the quality of texture patterns in 3D virtual urban models [22], reconstructing urban 3D models [35,42], or automating building change detection [92].…”

Section: Building Detail Detection and Classificationmentioning

confidence: 99%

Computer Vision-based Analysis of Buildings and Built Environments: A Systematic Review of Current Approaches

et al. 2023

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Analysing 88 sources published from 2011 to 2021, this paper presents a first systematic review of the computer vision-based analysis of buildings and the built environment. Its aim is to assess the potential of this research for architectural studies and the implications of a shift to a crossdisciplinarity approach between architecture and computer science for research problems, aims, processes, and applications. To this end, the types of algorithms and data sources used in the reviewed studies are discussed in respect to architectural applications such as a building classification, detail classification, qualitative environmental analysis, building condition survey, and building value estimation. Based on this, current research gaps and trends are identified, with two main research aims emerging. First, studies that use or optimise computer vision methods to automate time-consuming, labour-intensive, or complex tasks when analysing architectural image data. Second, work that explores the methodological benefits of machine learning approaches to overcome limitations of conventional analysis in order to investigate new questions about the built environment by finding patterns and relationships between visual, statistical, and qualitative data. The growing body of research offers new methods to architectural and design studies, with the paper identifying future challenges and directions of research.

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Section: Building Detail Detection and Classificationmentioning

confidence: 99%

Computer Vision-based Analysis of Buildings and Built Environments: A Systematic Review of Current Approaches

et al. 2023

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“…Of course, there are still numerous obstacles to overcome in order to accurately rebuild multi-view depth maps in urban landscapes, such as the presence of repeating textures and texture-poor places. To address the aforementioned issues, [260] proposes a multi-view 3D reconstruction (IMGTR) method based on image triangles. In [261], it proposes to generate high-quality digital surface models by combining many depth maps that were calculated using a dense image matching method.…”

Section: D Cnnmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

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“…Two triangles that have the same shape have the same angles in each triangle [21]. The 2D triangle in the image must not overlap, this triangle is 2 different planes [22]. Triangles similarity is shown in Figure 2 and the triangle similarity equation is shown in Eqns.…”

Section: Figure 1 Research Flowchartmentioning

confidence: 99%

“…Hu in his research used a similar triangle to reconstruct a map to explore information on flexibility and scalability in 3 dimensions (3D). Hu processed the digital image of the map by determining the appropriate object plane in terms of similarity, texture smoothness, and image boundaries [22].…”

Section: Introductionmentioning

confidence: 99%

Distance Estimation Between Camera and Shrimp Underwater Using Euclidian Distance and Triangles Similarity Algorithm

Setiawan¹,

Hadiyanto²,

Widodo³

2022

ISI

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Camera is the main tool for monitoring shrimp underwater with a noninvasive method. Distance of the shrimp underwater with the varying camera causes the monitoring of the estimated shrimp size to be less accurate. This study provides a new solution to detect the distance of shrimp underwater with a camera using the Euclidian distance and triangle similarity algorithm. The problem raised in this study is how to measure the distance of a shrimp underwater with a camera. The method used has several stages, including using the grayscale image method, image thresholding, edge detection, detection of Region of Interest (ROI), determining the position of shrimp coordinates, calculating the length of shrimp coordinates, camera calibration using the triangle similarity algorithm, calculating the estimated distance of shrimp with the camera. The study results obtained a focal length value of 1298.58, the value of the distance between the shrimp and the camera (D') for 5 positions of shrimp underwater from 50 cm – 19.86 cm, and the RE value from 0% - 0.13%. In conclusion, this method can be used to measure the estimated distance between of moving shrimp and a camera with a low error rate.

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IMGTR: Image-triangle based multi-view 3D reconstruction for urban scenes

Cited by 9 publications

References 26 publications

Computer Vision-based Analysis of Buildings and Built Environments: A Systematic Review of Current Approaches

Computer Vision-based Analysis of Buildings and Built Environments: A Systematic Review of Current Approaches

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Distance Estimation Between Camera and Shrimp Underwater Using Euclidian Distance and Triangles Similarity Algorithm

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