Fast 3D reconstruction method based on UAV photography

Jiang-an, Wang; Ma, Huang-Te; Wang, Chunmei; He, Yongjie

doi:10.4218/etrij.2017-0298

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…These raw images were then processed in SfM-based commercial software called Pix4D Mapper to generate a 3D point cloud. The SfM algorithm was used in the image processing step; a detailed explanation of the SfM algorithm can be found in several studies (42)(43)(44).…”

Section: Uav Flight and Data Acquisitionmentioning

confidence: 99%

Effectiveness of Training Sample and Features for Random Forest on Road Extraction from Unmanned Aerial Vehicle-Based Point Cloud

Biçici

Zeybek

2021

Transportation Research Record: Journal of the Transportation R

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The accuracy of random forest (RF) classification depends on several inputs. In this study, two primary inputs—training sample and features—are evaluated for road classification from an unmanned aerial vehicle-based point cloud. Training sample selection is a challenging step since the machine learning stage of the RF classification depends greatly on it. That is, an imbalanced training sample might dramatically decrease classification accuracy. Various criteria are defined to generate different types of training samples to evaluate the effectiveness of the training sample. There are several point features that can be used in RF classification under different circumstances. More features might increase the classification accuracy, however, in that case, the processing time is also increased. Point features such as RGB (red/green/blue), surface normals, curvature, omnivariance, planarity, linearity, surface variance, anisotropy, verticality, and ground/non-ground class are investigated in this study. Different training samples and sets of features are used in the RF to extract the road surface. The experiment is conducted on a local road without a raised curb located on a relatively steep hill. The accuracy assessment is conducted by comparing the model classification results with the manually extracted road surface point cloud. It is found that the accuracy increases up to around 4%–13%, and 95% overall accuracy was obtained when using convenient training samples and features.

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Section: Uav Flight and Data Acquisitionmentioning

confidence: 99%

Effectiveness of Training Sample and Features for Random Forest on Road Extraction from Unmanned Aerial Vehicle-Based Point Cloud

Biçici

Zeybek

2021

Transportation Research Record: Journal of the Transportation R

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“…Once the work has been completed, the UAVs are still useful for monitoring and inspecting buildings in use. The usual photogrammetric techniques are applied to buildingsin-use to generate three-dimensional, orthophotos and orthomosaic models of facades and roofs affected by various pathologies [66][67][68][69][70]. The newest methodologies are applied to the analysis of the structural health of buildings in other complex environments where there is no GPS coverage [71].…”

Section: Critical Literature Reviewmentioning

confidence: 99%

A Critical Review of Unmanned Aerial Vehicles (UAVs) Use in Architecture and Urbanism: Scientometric and Bibliometric Analysis

et al. 2021

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In recent years the use of UAVs (Unmanned aerial vehicles) have proliferated in the civil sector for purposes such as search and rescue, remote sensing or real-time monitoring of road traffic, among others. In the architecture, engineering and construction fields (AEC) UAVs have demonstrated to be an ideal technology due to their optimal performance in terms of time, precision, safety and cost. Given the rapid growth of interest in this technology, this research presents a critical review of the literature on the use of UAVs in architecture and urbanism to define the most widely used techniques and delimit the fields of application based on the experimentation published by the scientific community. A scientific mapping was carried out in two stages using the VOSviewerTM software: a scientometric and a bibliometric analysis. This technique allowed us to analyse a large body of literature and bibliographic data to obtain trends, patterns and directions of this domain of knowledge. Then, a literature review was presented, highlighting the relevant information identified in the previous analysis. The fields of application of UAVs were delimited and the most commonly used payload types and the most appropriate post-processing techniques were specified, depending on the aerial mission objective. The fields of application identified included different techniques related to the generation of 3D models, land mapping, construction site monitoring, building surveying to detect structural damage and energy losses and urban remote sensing. The literature review showed that UAVs provide a useful multi-tasking tool at any stage of an architectural project. These techniques can be applied to buildings or public spaces from the design and construction processes when the project is initiated to the later stages of maintenance and inspection of the building during its life cycle.

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“…In recent years, computer vision technology has made significant contributions to 3D modelling research (Biçici and Zeybek, 2021;Wang et al 2018;Zeybek and Şanlıoğlu, 2019). High-resolution digital images are transformed into highaccurate 3D models.…”

Section: Image Processingmentioning

confidence: 99%

“…For this, it is possible to generate dense point clouds with multi-image stereo (MVS) and Structurefrom-Motion (SfM) algorithms. A detailed explanation of the SfM algorithm can be found in several studies (Wang et al 2018;Zeybek and Şanlıoğlu, 2019). In this study, two different commercial SfM based software were used to produce a 3D point cloud.…”

Section: Image Processingmentioning

confidence: 99%

3D Dense Reconstruction of Road Surface from UAV Images and Comparison of SfM Based Software Performance

Zeybek¹,

Biçici²

2021

Turkish Journal of Remote Sensing and GIS

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Unmanned Aerial Vehicle (UAV) technology is one of the fastest-growing technologies especially used in image processing. Structure-from-Motion (SfM) based software are usually used to convert two-dimensional UAV-based images into three-dimensional (3D) data. Then, objects such as buildings, trees, and roads can be classified from the 3D data for further analysis. In this study, the road surface generated from 3D data was evaluated. There are several factors that affect the accuracy of the 3D data. In this study, two factors, namely UAV flight altitude and SfM based software, were evaluated. Two different flight altitudes, which were 35 meters and 50 meters, were used. It was found that the lower flights with closer altitudes did not make a significant difference on the results and produced similar results. Another factor is different SfM based software. Two well-known SfM based software were used in this study, which were the Agisoft Metashape and Pix4D Mapper. In this case study, it was found that the Agisoft Metashape software produced more accurate and faster results than Pix4D Mapper software.

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Fast 3D reconstruction method based on UAV photography

Cited by 12 publications

References 24 publications

Effectiveness of Training Sample and Features for Random Forest on Road Extraction from Unmanned Aerial Vehicle-Based Point Cloud

Effectiveness of Training Sample and Features for Random Forest on Road Extraction from Unmanned Aerial Vehicle-Based Point Cloud

A Critical Review of Unmanned Aerial Vehicles (UAVs) Use in Architecture and Urbanism: Scientometric and Bibliometric Analysis

3D Dense Reconstruction of Road Surface from UAV Images and Comparison of SfM Based Software Performance

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