Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses.However,existing discontinuity mapping algorithms are susceptible to noise,and the calculation results ca...Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses.However,existing discontinuity mapping algorithms are susceptible to noise,and the calculation results cannot be fed back to users timely.To address this issue,we proposed a human-machine interaction(HMI)method for discontinuity mapping.Users can help the algorithm identify the noise and make real-time result judgments and parameter adjustments.For this,a regular cube was selected to illustrate the workflows:(1)point cloud was acquired using remote sensing;(2)the HMI method was employed to select reference points and angle thresholds to detect group discontinuity;(3)individual discontinuities were extracted from the group discontinuity using a density-based cluster algorithm;and(4)the orientation of each discontinuity was measured based on a plane fitting algorithm.The method was applied to a well-studied highway road cut and a complex natural slope.The consistency of the computational results with field measurements demonstrates its good accuracy,and the average error in the dip direction and dip angle for both cases was less than 3.Finally,the computational time of the proposed method was compared with two other popular algorithms,and the reduction in computational time by tens of times proves its high computational efficiency.This method provides geologists and geological engineers with a new idea to map rapidly and accurately rock structures under large amounts of noises or unclear features.展开更多
This paper presents an automated method for discontinuity trace mapping using three-dimensional point clouds of rock mass surfaces.Specifically,the method consists of five steps:(1)detection of trace feature points by...This paper presents an automated method for discontinuity trace mapping using three-dimensional point clouds of rock mass surfaces.Specifically,the method consists of five steps:(1)detection of trace feature points by normal tensor voting theory,(2)co ntraction of trace feature points,(3)connection of trace feature points,(4)linearization of trace segments,and(5)connection of trace segments.A sensitivity analysis was then conducted to identify the optimal parameters of the proposed method.Three field cases,a natural rock mass outcrop and two excavated rock tunnel surfaces,were analyzed using the proposed method to evaluate its validity and efficiency.The results show that the proposed method is more efficient and accurate than the traditional trace mapping method,and the efficiency enhancement is more robust as the number of feature points increases.展开更多
Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materia...Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.展开更多
The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment.Traditional manual measurement is often dangerous or unreach...The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment.Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes.In contrast,unmanned aerial vehicle(UAV)photogrammetry is not limited by terrain conditions,and can efficiently collect high-precision three-dimensional(3D)point clouds of rock masses through all-round and multiangle photography for rock mass characterization.In this paper,a new method based on a 3D point cloud is proposed for discontinuity identification and refined rock block modeling.The method is based on four steps:(1)Establish a point cloud spatial topology,and calculate the point cloud normal vector and average point spacing based on several machine learning algorithms;(2)Extract discontinuities using the density-based spatial clustering of applications with noise(DBSCAN)algorithm and fit the discontinuity plane by combining principal component analysis(PCA)with the natural breaks(NB)method;(3)Propose a method of inserting points in the line segment to generate an embedded discontinuity point cloud;and(4)Adopt a Poisson reconstruction method for refined rock block modeling.The proposed method was applied to an outcrop of an ultrahigh steep rock slope and compared with the results of previous studies and manual surveys.The results show that the method can eliminate the influence of discontinuity undulations on the orientation measurement and describe the local concave-convex characteristics on the modeling of rock blocks.The calculation results are accurate and reliable,which can meet the practical requirements of engineering.展开更多
An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, clo...An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.展开更多
In the last two decades,significant research has been conducted in the field of automated extraction of rock mass discontinuity characteristics from three-dimensional(3D)models.This provides several methodologies for ...In the last two decades,significant research has been conducted in the field of automated extraction of rock mass discontinuity characteristics from three-dimensional(3D)models.This provides several methodologies for acquiring discontinuity measurements from 3D models,such as point clouds generated using laser scanning or photogrammetry.However,even with numerous automated and semiautomated methods presented in the literature,there is not one single method that can automatically characterize discontinuities accurately in a minimum of time.In this paper,we critically review all the existing methods proposed in the literature for the extraction of discontinuity characteristics such as joint sets and orientations,persistence,joint spacing,roughness and block size using point clouds,digital elevation maps,or meshes.As a result of this review,we identify the strengths and drawbacks of each method used for extracting those characteristics.We found that the approaches based on voxels and region growing are superior in extracting joint planes from 3D point clouds.Normal tensor voting with trace growth algorithm is a robust method for measuring joint trace length from 3D meshes.Spacing is estimated by calculating the perpendicular distance between joint planes.Several independent roughness indices are presented to quantify roughness from 3D surface models,but there is a need to incorporate these indices into automated methodologies.There is a lack of efficient algorithms for direct computation of block size from 3D rock mass surface models.展开更多
Automatic identificationof discontinuities is a key focus in rock slope research.Conventional methods typically target small areas,which limits efficiencyand applicability for complex discontinuities in large-scale ro...Automatic identificationof discontinuities is a key focus in rock slope research.Conventional methods typically target small areas,which limits efficiencyand applicability for complex discontinuities in large-scale rock slopes.This study uses multi-angle unmanned aerial vehicle(UAV)nap-of-the-object photogrammetry to construct a high-definitionthree-dimensional(3D)point cloud model of the slope.The edge-firstconnection algorithm identifiesall edge points of discontinuities in the point cloud and completes recognition through simple connection analysis.This method avoids the complex calculations required for sequentially identifying discontinuity edges in conventional methods and achieves significantacceleration through algorithm optimization and parallel computation support.Based on this algorithm,the RockDiscontinuity Identification(RD ID)software is developed and applied to identify numerous highly disordered discontinuities on the Xulong slope in the Jinsha River suture zone.Processing tens of millions of point clouds within approximately 2 h demonstrates exceptional computational efficiency.The automatic algorithm accurately identifiesnearly 80%of planar discontinuities,with orientations and trace lengths closely matching manual results,highlighting its potential for large-scale rock outcrop applications.Comparisons with region growing algorithms further emphasize its effectiveness and accuracy.However,the algorithm struggles to identify linear discontinuities,which are a major source of error.Additionally,high roughness and smooth edges of discontinuities affect recognition accuracy,indicating areas for further improvement.展开更多
When obtaining three-dimensional(3D)face point cloud data based on structured light,factors related to the environment,occlusion,and illumination intensity lead to holes in the collected data,which affect subsequent r...When obtaining three-dimensional(3D)face point cloud data based on structured light,factors related to the environment,occlusion,and illumination intensity lead to holes in the collected data,which affect subsequent recognition.In this study,we propose a hole-filling method based on stereo-matching technology combined with a B-spline.The algorithm uses phase information acquired during raster projection to locate holes in the point cloud,simultaneously extracting boundary point cloud sets.By registering the face point cloud data using the stereo-matching algorithm and the data collected using the raster projection method,some supplementary information points can be obtained at the holes.The shape of the B-spline curve can then be roughly described by a few key points,and the control points are put into the hole area as key points for iterative calculation of surface reconstruction.Simulations using smooth ceramic cups and human face models showed that our model can accurately reproduce details and accurately restore complex shapes on the test surfaces.Simulation results indicated the robustness of the method,which is able to fill holes on complex areas such as the inner side of the nose without a prior model.This approach also effectively supplements the hole information,and the patched point cloud is closer to the original data.This method could be used across a wide range of applications requiring accurate facial recognition.展开更多
Fusing three-dimensional(3D)and multispectral(MS)imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge.Acquiring high-quality 3D MS point clouds(3DM...Fusing three-dimensional(3D)and multispectral(MS)imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge.Acquiring high-quality 3D MS point clouds(3DMPCs)of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure.Here,we present a novel 3D spatial–spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field(NeREF)for radiometric calibration.This approach was used to acquire 3DMPCs of perilla,tomato,and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness(EWT)estimation.The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6%compared with the fixed viewpoints alone.The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error(RMSE)of 58.93%for extracted reflectance spectra.The RMSE for chlorophyll content and EWT predictions decreased by 21.25%and 14.13%using partial least squares regression with the generated 3DMPCs.Collectively,our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions,which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits,and thus will facilitate plant biology and genetic studies as well as crop breeding.展开更多
Segmentation of three-dimensional(3D)point clouds is fundamental in comprehending unstructured structural and morphological data.It plays a critical role in research related to plant phenomics,3D plant modeling,and fu...Segmentation of three-dimensional(3D)point clouds is fundamental in comprehending unstructured structural and morphological data.It plays a critical role in research related to plant phenomics,3D plant modeling,and functional-structural plant modeling.Although technologies for plant point cloud segmentation(PPCS)have advanced rapidly,there has been a lack of a systematic overview of the development process.This paper presents an overview of the progress made in 3D point cloud segmentation research in plants.It starts by discussing the methods used to acquire point clouds in plants,and analyzes the impact of point cloud resolution and quality on the segmentation task.It then introduces multi-scale point cloud segmentation in plants.The paper summarizes and analyzes traditional methods for PPCS,including the global and local features.This paper discusses the progress of machine learning-based segmentation on plant point clouds through supervised,unsupervised,and integrated approaches.It also summarizes the datasets that for PPCS using deep learning-oriented methods and explains the advantages and disadvantages of deep learning-based methods for projection-based,voxel-based,and point-based approaches respectively.Finally,the development of PPCS is discussed and prospected.Deep learning methods are predicted to become dominant in the field of PPCS,and 3D point cloud segmentation would develop towards more automated with higher resolution and precision.展开更多
Accurate and rapid wheat morphology reconstruction and trait collection are essential for selecting varieties,scientific cultivation,and precise management.A single perspective is limited by environmental obstructions...Accurate and rapid wheat morphology reconstruction and trait collection are essential for selecting varieties,scientific cultivation,and precise management.A single perspective is limited by environmental obstructions,hindering the collection of high-throughput phenotype data for wheat plants.Therefore,a rapid reconstruction method of multi-view threedimensional point cloud is proposed to realize the high-throughput and accurate identification of wheat phenotype.Firstly,taking wheat at the tillering stage as the experimental object,a multi-view acquisition system based on a RealSense sensor was constructed,and the point cloud data of wheat were obtained from 16 views.Secondly,a joint photometric and geometric objective was optimized,and space location was registered by colored Point Cloud Registration(colored)and Iterative Closest Point(ICP)algorithms.Furthermore,the Multiple View Stereo(MVS)algorithm was used to combine the depth image,RGB image,and spatial position obtained by coarse registration to enable the fine registration of multi-viewpoint clouds.Compared with the traditional Structure From Motion(SFM)-MVS algorithm,our proposed method is much faster,with an average reconstruction time of 33.82 s.Moreover,the wheat plant height,leaf length,leaf width,leaf area,and leaf angle of wheat were calculated based on the three-dimensional point cloud of the wheat plant.The experimental results showed that the determination coefficients of the method are 0.996,0.958,0.956,0.984,and 0.849,respectively.Finally,phenotypic information such as compact degree,convex hull volume,and average leaf area of different wheat varieties was analyzed and identified,proving that the method could capture the phenotypic differences between varieties and individuals.The proposed method provides a rapid approach to quantify wheat phenotypic traits,aiding breeding,scientific cultivation,and environmental management.展开更多
Smartphones are usually packed with a large number of features.An increasing number of researchers are paying attention to the technological capabilities of smartphones,which is a new topic and research interest.This ...Smartphones are usually packed with a large number of features.An increasing number of researchers are paying attention to the technological capabilities of smartphones,which is a new topic and research interest.This paper proposes a method using smartphones and digital photogrammetry to measure the discontinuity orientation of a rock mass.Smartphone photos satisfying a certain overlap rate provide an efficient method for generating point cloud models of rock outcrops based on image matching.Using the target and the generated point cloud model allows for determining actual geographic coordinates and the measurement of discontinuity orientations.The method proposed has been applied to two different study areas.The discontinuity orientations measured by the proposed method are compared with those measured by the manual method in two cases.The results show a good agreement,verifying the reliability and accuracy of the proposed method.The main contribution of this paper is to use knowledge of coordinate rotation to determine the actual geographic location of the model through a square target.The equipment used in this study is simple,and photogrammetric field surveys are easy to carry out.展开更多
This paper describes the development of an intelligent automated control system of a robot manipulator for plasma treatment of medical implants with complex shapes.The two-layer coatings from the Ti wire and hydroxyap...This paper describes the development of an intelligent automated control system of a robot manipulator for plasma treatment of medical implants with complex shapes.The two-layer coatings from the Ti wire and hydroxyapatite powders are applied on the surface of Ti medical implants by microplasma spraying to increase the biocompatibility of implants.The coating process requires precise control of a number of parameters,particularly the plasma spray distance and plasma jet traverse velocity.Thus,the development of the robotic plasma surface treatment involves automated path planning.The key idea of the proposed intelligent automatic control system is the use of data of preliminary three-dimensional (3D) scanning of the processed implant by the robot manipulator.The segmentation algorithm of the point cloud from laser scanning of the surface is developed.This methodology is suitable for robotic 3D scanning systems with both non-contact laser distance sensors and video cameras,used in additive manufacturing and medicine.展开更多
Rice quality directly affects the final rice yield.In order to achieve rapid,non-destructive testing of rice seeds,this paper combines the three-dimensional laser scanning technology and back propagation(BP)neural net...Rice quality directly affects the final rice yield.In order to achieve rapid,non-destructive testing of rice seeds,this paper combines the three-dimensional laser scanning technology and back propagation(BP)neural network algorithm to build a rice seeds identification platform.The information on rice seed surface is collected from four angles and processed using Geomagic Studio software.Based on the noise filtering,smoothing of the point cloud,vulnerability repair,and downsampling,the three-dimensional(3D)morphological characteristics of a rice seed surface,and the projection features of the main plane cross-section are obtained through the calculation of the features.The experiments were performed on five rice varieties,including Da Hua aromatic glutinous,Hong ShiⅠ,Tian You VIII,Xin Dao X,and Yu Jing VI.The resulting input vector consisted respectively of:(1)nine 3D morphological surface features,(2)nine projection features of the main cross-section plane of rice,and(3)all of the above features.The results showed that for an input vector consisting of nine surface 3D morphological features,the recognition rate of the five rice varieties was 95%,96%,87%,93%,and 89%,respectively;for an input vector consisting of nine projection features of the main cross-section plane of rice seeds,the recognition rate was 96%,96%,90%,92%,and 89%,respectively;and lastly,for an input vector consisting of all the features,the highest recognition rate of 96%,97%,91%,94%,and 90%,respectively,was achieved.The analysis showed that rice varieties could be identified by using 3D laser scanning.Therefore,the proposed method can improve the accuracy of rice varieties identification.展开更多
基金supported by the National Key R&D Program of China(No.2023YFC3081200)the National Natural Science Foundation of China(No.42077264)the Scientific Research Project of PowerChina Huadong Engineering Corporation Limited(HDEC-2022-0301).
文摘Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses.However,existing discontinuity mapping algorithms are susceptible to noise,and the calculation results cannot be fed back to users timely.To address this issue,we proposed a human-machine interaction(HMI)method for discontinuity mapping.Users can help the algorithm identify the noise and make real-time result judgments and parameter adjustments.For this,a regular cube was selected to illustrate the workflows:(1)point cloud was acquired using remote sensing;(2)the HMI method was employed to select reference points and angle thresholds to detect group discontinuity;(3)individual discontinuities were extracted from the group discontinuity using a density-based cluster algorithm;and(4)the orientation of each discontinuity was measured based on a plane fitting algorithm.The method was applied to a well-studied highway road cut and a complex natural slope.The consistency of the computational results with field measurements demonstrates its good accuracy,and the average error in the dip direction and dip angle for both cases was less than 3.Finally,the computational time of the proposed method was compared with two other popular algorithms,and the reduction in computational time by tens of times proves its high computational efficiency.This method provides geologists and geological engineers with a new idea to map rapidly and accurately rock structures under large amounts of noises or unclear features.
基金supported by the Special Fund for Basic Research on Scientific Instruments of the National Natural Science Foundation of China(Grant No.4182780021)Emeishan-Hanyuan Highway ProgramTaihang Mountain Highway Program。
文摘This paper presents an automated method for discontinuity trace mapping using three-dimensional point clouds of rock mass surfaces.Specifically,the method consists of five steps:(1)detection of trace feature points by normal tensor voting theory,(2)co ntraction of trace feature points,(3)connection of trace feature points,(4)linearization of trace segments,and(5)connection of trace segments.A sensitivity analysis was then conducted to identify the optimal parameters of the proposed method.Three field cases,a natural rock mass outcrop and two excavated rock tunnel surfaces,were analyzed using the proposed method to evaluate its validity and efficiency.The results show that the proposed method is more efficient and accurate than the traditional trace mapping method,and the efficiency enhancement is more robust as the number of feature points increases.
基金funded by the National Natural Science Foundation of China(42071014).
文摘Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.
基金supported by the National Natural Science Foundation of China(Grant Nos.41941017 and 42177139)Graduate Innovation Fund of Jilin University(Grant No.2024CX099)。
文摘The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment.Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes.In contrast,unmanned aerial vehicle(UAV)photogrammetry is not limited by terrain conditions,and can efficiently collect high-precision three-dimensional(3D)point clouds of rock masses through all-round and multiangle photography for rock mass characterization.In this paper,a new method based on a 3D point cloud is proposed for discontinuity identification and refined rock block modeling.The method is based on four steps:(1)Establish a point cloud spatial topology,and calculate the point cloud normal vector and average point spacing based on several machine learning algorithms;(2)Extract discontinuities using the density-based spatial clustering of applications with noise(DBSCAN)algorithm and fit the discontinuity plane by combining principal component analysis(PCA)with the natural breaks(NB)method;(3)Propose a method of inserting points in the line segment to generate an embedded discontinuity point cloud;and(4)Adopt a Poisson reconstruction method for refined rock block modeling.The proposed method was applied to an outcrop of an ultrahigh steep rock slope and compared with the results of previous studies and manual surveys.The results show that the method can eliminate the influence of discontinuity undulations on the orientation measurement and describe the local concave-convex characteristics on the modeling of rock blocks.The calculation results are accurate and reliable,which can meet the practical requirements of engineering.
基金Project(51274250)supported by the National Natural Science Foundation of ChinaProject(2012BAK09B02-05)supported by the National Key Technology R&D Program during the 12th Five-year Plan of China
文摘An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.
基金funded by the U.S.National Institute for Occupational Safety and Health(NIOSH)under the Contract No.75D30119C06044。
文摘In the last two decades,significant research has been conducted in the field of automated extraction of rock mass discontinuity characteristics from three-dimensional(3D)models.This provides several methodologies for acquiring discontinuity measurements from 3D models,such as point clouds generated using laser scanning or photogrammetry.However,even with numerous automated and semiautomated methods presented in the literature,there is not one single method that can automatically characterize discontinuities accurately in a minimum of time.In this paper,we critically review all the existing methods proposed in the literature for the extraction of discontinuity characteristics such as joint sets and orientations,persistence,joint spacing,roughness and block size using point clouds,digital elevation maps,or meshes.As a result of this review,we identify the strengths and drawbacks of each method used for extracting those characteristics.We found that the approaches based on voxels and region growing are superior in extracting joint planes from 3D point clouds.Normal tensor voting with trace growth algorithm is a robust method for measuring joint trace length from 3D meshes.Spacing is estimated by calculating the perpendicular distance between joint planes.Several independent roughness indices are presented to quantify roughness from 3D surface models,but there is a need to incorporate these indices into automated methodologies.There is a lack of efficient algorithms for direct computation of block size from 3D rock mass surface models.
基金supported by the National Key R&D Program of China(Grant No.2022YFC3080200)the China Postdoctoral Science Foundation(Grant No.2023M731264)the Science and Technology Development Plan Project of Jilin Province,China(Grant No.20250602007RC).
文摘Automatic identificationof discontinuities is a key focus in rock slope research.Conventional methods typically target small areas,which limits efficiencyand applicability for complex discontinuities in large-scale rock slopes.This study uses multi-angle unmanned aerial vehicle(UAV)nap-of-the-object photogrammetry to construct a high-definitionthree-dimensional(3D)point cloud model of the slope.The edge-firstconnection algorithm identifiesall edge points of discontinuities in the point cloud and completes recognition through simple connection analysis.This method avoids the complex calculations required for sequentially identifying discontinuity edges in conventional methods and achieves significantacceleration through algorithm optimization and parallel computation support.Based on this algorithm,the RockDiscontinuity Identification(RD ID)software is developed and applied to identify numerous highly disordered discontinuities on the Xulong slope in the Jinsha River suture zone.Processing tens of millions of point clouds within approximately 2 h demonstrates exceptional computational efficiency.The automatic algorithm accurately identifiesnearly 80%of planar discontinuities,with orientations and trace lengths closely matching manual results,highlighting its potential for large-scale rock outcrop applications.Comparisons with region growing algorithms further emphasize its effectiveness and accuracy.However,the algorithm struggles to identify linear discontinuities,which are a major source of error.Additionally,high roughness and smooth edges of discontinuities affect recognition accuracy,indicating areas for further improvement.
基金supported by the National Natural Science Foundation of China(No.61405034)the Special Project on Basic Research of Frontier Leading Technology of Jiangsu Province,China(No.BK20192004C)+1 种基金the Shenzhen Science and Technology Innovation Committee(No.JCYJ20180306174455080)the Natural Science Foundation of Jiangsu Province,China(No.BK20181269)。
文摘When obtaining three-dimensional(3D)face point cloud data based on structured light,factors related to the environment,occlusion,and illumination intensity lead to holes in the collected data,which affect subsequent recognition.In this study,we propose a hole-filling method based on stereo-matching technology combined with a B-spline.The algorithm uses phase information acquired during raster projection to locate holes in the point cloud,simultaneously extracting boundary point cloud sets.By registering the face point cloud data using the stereo-matching algorithm and the data collected using the raster projection method,some supplementary information points can be obtained at the holes.The shape of the B-spline curve can then be roughly described by a few key points,and the control points are put into the hole area as key points for iterative calculation of surface reconstruction.Simulations using smooth ceramic cups and human face models showed that our model can accurately reproduce details and accurately restore complex shapes on the test surfaces.Simulation results indicated the robustness of the method,which is able to fill holes on complex areas such as the inner side of the nose without a prior model.This approach also effectively supplements the hole information,and the patched point cloud is closer to the original data.This method could be used across a wide range of applications requiring accurate facial recognition.
基金funded by the National Natural Science Foundation of China(32371985)the Fundamental Research Funds for the Central Universities,China(226-2022-00217).
文摘Fusing three-dimensional(3D)and multispectral(MS)imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge.Acquiring high-quality 3D MS point clouds(3DMPCs)of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure.Here,we present a novel 3D spatial–spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field(NeREF)for radiometric calibration.This approach was used to acquire 3DMPCs of perilla,tomato,and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness(EWT)estimation.The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6%compared with the fixed viewpoints alone.The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error(RMSE)of 58.93%for extracted reflectance spectra.The RMSE for chlorophyll content and EWT predictions decreased by 21.25%and 14.13%using partial least squares regression with the generated 3DMPCs.Collectively,our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions,which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits,and thus will facilitate plant biology and genetic studies as well as crop breeding.
基金supported by National Key Research and Development Program of China(2022YFD2001003)the National Natural Science Foundation of China(32071891)+1 种基金Science and Technology Innovation Special Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences(KJCX20220401)the earmarked fund(CARS-02 and CARS-54).
文摘Segmentation of three-dimensional(3D)point clouds is fundamental in comprehending unstructured structural and morphological data.It plays a critical role in research related to plant phenomics,3D plant modeling,and functional-structural plant modeling.Although technologies for plant point cloud segmentation(PPCS)have advanced rapidly,there has been a lack of a systematic overview of the development process.This paper presents an overview of the progress made in 3D point cloud segmentation research in plants.It starts by discussing the methods used to acquire point clouds in plants,and analyzes the impact of point cloud resolution and quality on the segmentation task.It then introduces multi-scale point cloud segmentation in plants.The paper summarizes and analyzes traditional methods for PPCS,including the global and local features.This paper discusses the progress of machine learning-based segmentation on plant point clouds through supervised,unsupervised,and integrated approaches.It also summarizes the datasets that for PPCS using deep learning-oriented methods and explains the advantages and disadvantages of deep learning-based methods for projection-based,voxel-based,and point-based approaches respectively.Finally,the development of PPCS is discussed and prospected.Deep learning methods are predicted to become dominant in the field of PPCS,and 3D point cloud segmentation would develop towards more automated with higher resolution and precision.
基金financially supported by Shandong Provincial Key Research and Development Program(Grant No.2022LZGCQY002,2021LZGC013,2023TZXD004).
文摘Accurate and rapid wheat morphology reconstruction and trait collection are essential for selecting varieties,scientific cultivation,and precise management.A single perspective is limited by environmental obstructions,hindering the collection of high-throughput phenotype data for wheat plants.Therefore,a rapid reconstruction method of multi-view threedimensional point cloud is proposed to realize the high-throughput and accurate identification of wheat phenotype.Firstly,taking wheat at the tillering stage as the experimental object,a multi-view acquisition system based on a RealSense sensor was constructed,and the point cloud data of wheat were obtained from 16 views.Secondly,a joint photometric and geometric objective was optimized,and space location was registered by colored Point Cloud Registration(colored)and Iterative Closest Point(ICP)algorithms.Furthermore,the Multiple View Stereo(MVS)algorithm was used to combine the depth image,RGB image,and spatial position obtained by coarse registration to enable the fine registration of multi-viewpoint clouds.Compared with the traditional Structure From Motion(SFM)-MVS algorithm,our proposed method is much faster,with an average reconstruction time of 33.82 s.Moreover,the wheat plant height,leaf length,leaf width,leaf area,and leaf angle of wheat were calculated based on the three-dimensional point cloud of the wheat plant.The experimental results showed that the determination coefficients of the method are 0.996,0.958,0.956,0.984,and 0.849,respectively.Finally,phenotypic information such as compact degree,convex hull volume,and average leaf area of different wheat varieties was analyzed and identified,proving that the method could capture the phenotypic differences between varieties and individuals.The proposed method provides a rapid approach to quantify wheat phenotypic traits,aiding breeding,scientific cultivation,and environmental management.
基金supported by the National Natural Science Foundation of China(Grant No.51769014),which is gratefully acknowledged.
文摘Smartphones are usually packed with a large number of features.An increasing number of researchers are paying attention to the technological capabilities of smartphones,which is a new topic and research interest.This paper proposes a method using smartphones and digital photogrammetry to measure the discontinuity orientation of a rock mass.Smartphone photos satisfying a certain overlap rate provide an efficient method for generating point cloud models of rock outcrops based on image matching.Using the target and the generated point cloud model allows for determining actual geographic coordinates and the measurement of discontinuity orientations.The method proposed has been applied to two different study areas.The discontinuity orientations measured by the proposed method are compared with those measured by the manual method in two cases.The results show a good agreement,verifying the reliability and accuracy of the proposed method.The main contribution of this paper is to use knowledge of coordinate rotation to determine the actual geographic location of the model through a square target.The equipment used in this study is simple,and photogrammetric field surveys are easy to carry out.
基金supported by the Science Committee of RK MES under the Grant No. AP05130525。
文摘This paper describes the development of an intelligent automated control system of a robot manipulator for plasma treatment of medical implants with complex shapes.The two-layer coatings from the Ti wire and hydroxyapatite powders are applied on the surface of Ti medical implants by microplasma spraying to increase the biocompatibility of implants.The coating process requires precise control of a number of parameters,particularly the plasma spray distance and plasma jet traverse velocity.Thus,the development of the robotic plasma surface treatment involves automated path planning.The key idea of the proposed intelligent automatic control system is the use of data of preliminary three-dimensional (3D) scanning of the processed implant by the robot manipulator.The segmentation algorithm of the point cloud from laser scanning of the surface is developed.This methodology is suitable for robotic 3D scanning systems with both non-contact laser distance sensors and video cameras,used in additive manufacturing and medicine.
基金The authors are very grateful for the support provided by the N ational Natural Science Foundation of China(Grant No.51507081)the Fundamental Research Funds for the Central Universities(KJ QN201623)the National Key Research and Development Pro gram of China(2017YFD0700800).
文摘Rice quality directly affects the final rice yield.In order to achieve rapid,non-destructive testing of rice seeds,this paper combines the three-dimensional laser scanning technology and back propagation(BP)neural network algorithm to build a rice seeds identification platform.The information on rice seed surface is collected from four angles and processed using Geomagic Studio software.Based on the noise filtering,smoothing of the point cloud,vulnerability repair,and downsampling,the three-dimensional(3D)morphological characteristics of a rice seed surface,and the projection features of the main plane cross-section are obtained through the calculation of the features.The experiments were performed on five rice varieties,including Da Hua aromatic glutinous,Hong ShiⅠ,Tian You VIII,Xin Dao X,and Yu Jing VI.The resulting input vector consisted respectively of:(1)nine 3D morphological surface features,(2)nine projection features of the main cross-section plane of rice,and(3)all of the above features.The results showed that for an input vector consisting of nine surface 3D morphological features,the recognition rate of the five rice varieties was 95%,96%,87%,93%,and 89%,respectively;for an input vector consisting of nine projection features of the main cross-section plane of rice seeds,the recognition rate was 96%,96%,90%,92%,and 89%,respectively;and lastly,for an input vector consisting of all the features,the highest recognition rate of 96%,97%,91%,94%,and 90%,respectively,was achieved.The analysis showed that rice varieties could be identified by using 3D laser scanning.Therefore,the proposed method can improve the accuracy of rice varieties identification.
