Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in ed...Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.展开更多
In order to improve the accuracy of the photogrammetric joint roughness coefficient(JRC)value,the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite e...In order to improve the accuracy of the photogrammetric joint roughness coefficient(JRC)value,the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite error model.Firstly,according to the depth map-based photogrammetric theory,the estimation of JRC from a three-dimensional(3D)digital surface model of rock discontinuities was presented.Secondly,an automatic shooting parameter selection algorithm was novelly proposed to establish the 3D model dataset of rock discontinuities with varying shooting parameters and target sizes.Meanwhile,the photogrammetric tests were performed with custom-built equipment capable of adjusting baseline lengths,and a total of 36 sets of JRC data was gathered via a combination of laboratory and field tests.Then,by combining the theory of point cloud coordinate computation error with the equation of JRC calculation,a composite error model controlled by the shooting parameters was proposed.This newly proposed model was validated via the 3D model dataset,demonstrating the capability to correct initially obtained JRC values solely based on shooting parameters.Furthermore,the implementation of this correction can significantly reduce errors in JRC values obtained via photographic measurement.Subsequently,our proposed error model was integrated into the shooting parameter selection algorithm,thus improving the rationality and convenience of selecting suitable shooting parameter combinations when dealing with target rock masses with different sizes.Moreover,the optimal combination of three shooting parameters was offered.JRC values resulting from various combinations of shooting parameters were verified by comparing them with 3D laser scan data.Finally,the application scope and limitations of the newly proposed approach were further addressed.展开更多
0 INTRODUCTION Rock masses are inherently discontinuous,with fractures and joints governing their mechanical behavior and stability(Liu et al.,2024;Shang et al.,2018;Lisjak and Grasselli,2014;Scholtès and Donz...0 INTRODUCTION Rock masses are inherently discontinuous,with fractures and joints governing their mechanical behavior and stability(Liu et al.,2024;Shang et al.,2018;Lisjak and Grasselli,2014;Scholtès and Donzé,2012;Jiang et al.,2009;Pine et al.,2006;Aydan et al.,1989).展开更多
Stem volume estimation is crucial in forest ecology and management,particularly for timber harvesting strategies and carbon stock assessments.This study aimed to develop a variable-exponent taper equation specifically...Stem volume estimation is crucial in forest ecology and management,particularly for timber harvesting strategies and carbon stock assessments.This study aimed to develop a variable-exponent taper equation specifically tailored to savanna tree species using close-range photogrammetry(CRP)data and to evaluate its performance against conventional volume equations for stem volume estimation.A dataset of 30 trees across five dominant savanna species was used to fit the taper model,which was validated using a separate dataset of 322 trees from 14 species.The results demonstrated significant improvements in volume estimation accuracy when using the taper equation.At the tree level,the root mean square error(RMSE)decreased by 47%,from 598 to 319 dm^(3),and the mean absolute bias(MAB)by 48%,from 328 to 172 dm3,compared to volume equations.Similarly,at the plot level,RMSE was reduced by 42% and MAB by 40%.The model performed well for species with regular forms.However,species with irregular tapers exhibited higher errors,reflecting the challenges of modeling stem forms of mixed species.The use of CRP proved valuable,providing high-resolution diameter measurements that improved model parameterization.This study underscores the importance of advanced data collection methods for enhancing taper model accuracy and suggests that further species-specific adjustments are needed to improve performance for species with irregular forms.The findings support the broader application of taper equations for improving stem volume estimates in savanna ecosystems,contributing to better forest management and resource monitoring practices.展开更多
This study provides the first systematic evaluation of image resolution’s effect (50-300 PPI, pixels per inch) on UAV (unmanned aerial vehicle)-based digital close-range photogrammetry accuracy in civil engineering a...This study provides the first systematic evaluation of image resolution’s effect (50-300 PPI, pixels per inch) on UAV (unmanned aerial vehicle)-based digital close-range photogrammetry accuracy in civil engineering applications, such as infrastructure monitoring and heritage preservation. Using a high-resolution UAV with a 20 MP (MegaPixels) sensor, four images of a brick wall test field were captured and processed in Agisoft Metashape, with resolutions compared against Leica T2002 theodolite measurements (1.0 mm accuracy). Advanced statistical methods (ANOVA (analysis of variance), Tukey tests, Monte Carlo simulations) and ground control points validated the results. Accuracy improved from 25 mm at 50 PPI to 5 mm at 150 PPI (p < 0.01), plateauing at 4 mm beyond 200 PPI, while 150 PPI reduced processing time by 62% compared to 300 PPI. Unlike prior studies, this research uniquely isolates resolution effects in a controlled civil engineering context, offering a novel 150 PPI threshold that balances precision and efficiency. This threshold supports Saudi Vision 2030’s smart infrastructure goals for megaprojects like NEOM, providing a scalable framework for global applications. Future research should leverage deep learning to optimize resolutions in dynamic environments.展开更多
The estimation of orientation parameters and correction of lens distortion are crucial problems in the field of Unmanned Aerial Vehicles(UAVs)photogrammetry.In recent years,the utilization of UAVs for aerial photogram...The estimation of orientation parameters and correction of lens distortion are crucial problems in the field of Unmanned Aerial Vehicles(UAVs)photogrammetry.In recent years,the utilization of UAVs for aerial photogrammetry has witnessed a surge in popularity.Typically,UAVs are equipped with low-cost non-metric cameras and a Position and Orientation System(POS).Unfortunately,the Interior Orientation Parameters(IOPs)of the non-metric cameras are not fixed.Whether the lens distortions are large or small,they effect the image coordinates accordingly.Additionally,Inertial Measurement Units(IMUs)often have observation errors.To address these challenges and improve parameter estimation for UAVs Light Detection and Ranging(LiDAR)and photogrammetry,this paper analyzes the accuracy of POS observations obtained from Global Navigation Satellite System Real Time Kinematic(GNSS-RTK)and IMU data.A method that incorporates additional known conditions for parameter estimation,a series of algorithms to simultaneously solve for IOPs,Exterior Orientation Parameters(EOPs),and camera lens distortion correction parameters are proposed.Extensive experiments demonstrate that the coordinates measured by GNSS-RTK can be directly used as linear EOPs;however,angular EOP measurements from IMUs exhibit relatively large errors compared to adjustment results and require correction during the adjustment process.The IOPs of non-metric cameras vary slightly between images but need to be treated as unknown parameters in high precision applications.Furthermore,it is found that the Ebner systematic error model is sensitive to the choice of the magnification parameter of the photographic baseline length in images,it should be set as less than or equal to one third of the photographic baseline to ensure stable solutions.展开更多
[Objective] The aim was to explore the measurement of coordinate parameter by multi-baseline digital close-range photogrammetry system.[Method] The 3-dimensional coordinate of 8-year-old Jujube was measured by using L...[Objective] The aim was to explore the measurement of coordinate parameter by multi-baseline digital close-range photogrammetry system.[Method] The 3-dimensional coordinate of 8-year-old Jujube was measured by using Lensphoto multi-baseline digital close-range photogrammetry system,and through comparing with measured data of Total Station,the error and accuracy of photogrammetry data were analyzed.[Result] The absolute error of X,Y and Z coordinate was 0-0.014,0-0.018 and 0-0.004 m respectively,and the relative error of X,Y and Z coordinate was less than 0.145%.The significance test of pairs for the photogrammetry data and measured data of Total Station indicated that the space coordinate data of stumpage were accurately measured by using the multi-baseline digital close-range photogrammetry method,and the photogrammetry data meet the need of space coordinate measurement for virtual plant growth simulation.[Conclusion] This study had provided theoretical basis for the growth measurement of virtual plant growth simulation.展开更多
针对内埋舱武器性能鉴定试飞中机弹分离相对位姿测量需求,提出了一种基于高速影像的内埋舱武器分离位姿测量方法。通过加装带有弯管镜头的高速摄像机阵列,实现内埋弹舱狭小空间内弹体分离全过程高速影像数据的分段获取;采用结合机载空...针对内埋舱武器性能鉴定试飞中机弹分离相对位姿测量需求,提出了一种基于高速影像的内埋舱武器分离位姿测量方法。通过加装带有弯管镜头的高速摄像机阵列,实现内埋弹舱狭小空间内弹体分离全过程高速影像数据的分段获取;采用结合机载空间参考点不确定性的相机外参解算、基于You Only Look Once version 8(YOLOv8)的标志点智能检测、基于边缘灰度梯度正交迭代的十字标中心坐标自动提取、直线约束下的多视角非交叠影像测量等方法,实现机载高速摄像机分布快速标定、小视场成像条件下弹体表面标志点亚像素坐标自动提取、机载高速摄像机抖动下的外参动态修正以及武器分离相对位姿分段测量等功能。经地面试验验证,该方法位置解算均方根误差不大于2 mm,满足飞行试验测试精度要求。展开更多
文摘Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.52225904 and 52039007)the Fundamental Research Funds for the Central Universities,CHD(Grant No.300102212207).
文摘In order to improve the accuracy of the photogrammetric joint roughness coefficient(JRC)value,the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite error model.Firstly,according to the depth map-based photogrammetric theory,the estimation of JRC from a three-dimensional(3D)digital surface model of rock discontinuities was presented.Secondly,an automatic shooting parameter selection algorithm was novelly proposed to establish the 3D model dataset of rock discontinuities with varying shooting parameters and target sizes.Meanwhile,the photogrammetric tests were performed with custom-built equipment capable of adjusting baseline lengths,and a total of 36 sets of JRC data was gathered via a combination of laboratory and field tests.Then,by combining the theory of point cloud coordinate computation error with the equation of JRC calculation,a composite error model controlled by the shooting parameters was proposed.This newly proposed model was validated via the 3D model dataset,demonstrating the capability to correct initially obtained JRC values solely based on shooting parameters.Furthermore,the implementation of this correction can significantly reduce errors in JRC values obtained via photographic measurement.Subsequently,our proposed error model was integrated into the shooting parameter selection algorithm,thus improving the rationality and convenience of selecting suitable shooting parameter combinations when dealing with target rock masses with different sizes.Moreover,the optimal combination of three shooting parameters was offered.JRC values resulting from various combinations of shooting parameters were verified by comparing them with 3D laser scan data.Finally,the application scope and limitations of the newly proposed approach were further addressed.
基金supported by the National Key R&D Program of China(No.2022YFC3080200)。
文摘0 INTRODUCTION Rock masses are inherently discontinuous,with fractures and joints governing their mechanical behavior and stability(Liu et al.,2024;Shang et al.,2018;Lisjak and Grasselli,2014;Scholtès and Donzé,2012;Jiang et al.,2009;Pine et al.,2006;Aydan et al.,1989).
基金partially funded by the International Foundation for Science(Grant No:I-1-D-6066-1).
文摘Stem volume estimation is crucial in forest ecology and management,particularly for timber harvesting strategies and carbon stock assessments.This study aimed to develop a variable-exponent taper equation specifically tailored to savanna tree species using close-range photogrammetry(CRP)data and to evaluate its performance against conventional volume equations for stem volume estimation.A dataset of 30 trees across five dominant savanna species was used to fit the taper model,which was validated using a separate dataset of 322 trees from 14 species.The results demonstrated significant improvements in volume estimation accuracy when using the taper equation.At the tree level,the root mean square error(RMSE)decreased by 47%,from 598 to 319 dm^(3),and the mean absolute bias(MAB)by 48%,from 328 to 172 dm3,compared to volume equations.Similarly,at the plot level,RMSE was reduced by 42% and MAB by 40%.The model performed well for species with regular forms.However,species with irregular tapers exhibited higher errors,reflecting the challenges of modeling stem forms of mixed species.The use of CRP proved valuable,providing high-resolution diameter measurements that improved model parameterization.This study underscores the importance of advanced data collection methods for enhancing taper model accuracy and suggests that further species-specific adjustments are needed to improve performance for species with irregular forms.The findings support the broader application of taper equations for improving stem volume estimates in savanna ecosystems,contributing to better forest management and resource monitoring practices.
文摘This study provides the first systematic evaluation of image resolution’s effect (50-300 PPI, pixels per inch) on UAV (unmanned aerial vehicle)-based digital close-range photogrammetry accuracy in civil engineering applications, such as infrastructure monitoring and heritage preservation. Using a high-resolution UAV with a 20 MP (MegaPixels) sensor, four images of a brick wall test field were captured and processed in Agisoft Metashape, with resolutions compared against Leica T2002 theodolite measurements (1.0 mm accuracy). Advanced statistical methods (ANOVA (analysis of variance), Tukey tests, Monte Carlo simulations) and ground control points validated the results. Accuracy improved from 25 mm at 50 PPI to 5 mm at 150 PPI (p < 0.01), plateauing at 4 mm beyond 200 PPI, while 150 PPI reduced processing time by 62% compared to 300 PPI. Unlike prior studies, this research uniquely isolates resolution effects in a controlled civil engineering context, offering a novel 150 PPI threshold that balances precision and efficiency. This threshold supports Saudi Vision 2030’s smart infrastructure goals for megaprojects like NEOM, providing a scalable framework for global applications. Future research should leverage deep learning to optimize resolutions in dynamic environments.
基金Natural Science Foundation of Hunan Province,China(No.2024JJ8335)Open Topic of Hunan Geospatial Information Engineering and Technology Research Center,China(No.HNGIET2023004).
文摘The estimation of orientation parameters and correction of lens distortion are crucial problems in the field of Unmanned Aerial Vehicles(UAVs)photogrammetry.In recent years,the utilization of UAVs for aerial photogrammetry has witnessed a surge in popularity.Typically,UAVs are equipped with low-cost non-metric cameras and a Position and Orientation System(POS).Unfortunately,the Interior Orientation Parameters(IOPs)of the non-metric cameras are not fixed.Whether the lens distortions are large or small,they effect the image coordinates accordingly.Additionally,Inertial Measurement Units(IMUs)often have observation errors.To address these challenges and improve parameter estimation for UAVs Light Detection and Ranging(LiDAR)and photogrammetry,this paper analyzes the accuracy of POS observations obtained from Global Navigation Satellite System Real Time Kinematic(GNSS-RTK)and IMU data.A method that incorporates additional known conditions for parameter estimation,a series of algorithms to simultaneously solve for IOPs,Exterior Orientation Parameters(EOPs),and camera lens distortion correction parameters are proposed.Extensive experiments demonstrate that the coordinates measured by GNSS-RTK can be directly used as linear EOPs;however,angular EOP measurements from IMUs exhibit relatively large errors compared to adjustment results and require correction during the adjustment process.The IOPs of non-metric cameras vary slightly between images but need to be treated as unknown parameters in high precision applications.Furthermore,it is found that the Ebner systematic error model is sensitive to the choice of the magnification parameter of the photographic baseline length in images,it should be set as less than or equal to one third of the photographic baseline to ensure stable solutions.
基金Supported by National Natural Science Foundation of China(30770401)National Eleventh Five-Year Plan for Forestry Scienceand Technology Support Topics(2006BADO3A0505)~~
文摘[Objective] The aim was to explore the measurement of coordinate parameter by multi-baseline digital close-range photogrammetry system.[Method] The 3-dimensional coordinate of 8-year-old Jujube was measured by using Lensphoto multi-baseline digital close-range photogrammetry system,and through comparing with measured data of Total Station,the error and accuracy of photogrammetry data were analyzed.[Result] The absolute error of X,Y and Z coordinate was 0-0.014,0-0.018 and 0-0.004 m respectively,and the relative error of X,Y and Z coordinate was less than 0.145%.The significance test of pairs for the photogrammetry data and measured data of Total Station indicated that the space coordinate data of stumpage were accurately measured by using the multi-baseline digital close-range photogrammetry method,and the photogrammetry data meet the need of space coordinate measurement for virtual plant growth simulation.[Conclusion] This study had provided theoretical basis for the growth measurement of virtual plant growth simulation.
文摘针对内埋舱武器性能鉴定试飞中机弹分离相对位姿测量需求,提出了一种基于高速影像的内埋舱武器分离位姿测量方法。通过加装带有弯管镜头的高速摄像机阵列,实现内埋弹舱狭小空间内弹体分离全过程高速影像数据的分段获取;采用结合机载空间参考点不确定性的相机外参解算、基于You Only Look Once version 8(YOLOv8)的标志点智能检测、基于边缘灰度梯度正交迭代的十字标中心坐标自动提取、直线约束下的多视角非交叠影像测量等方法,实现机载高速摄像机分布快速标定、小视场成像条件下弹体表面标志点亚像素坐标自动提取、机载高速摄像机抖动下的外参动态修正以及武器分离相对位姿分段测量等功能。经地面试验验证,该方法位置解算均方根误差不大于2 mm,满足飞行试验测试精度要求。
