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
Fish morphological phenotypes are important resources in artificial breeding,functional gene mapping,and population-based studies in aquaculture and ecology.Traditional morphological measurement of phenotypes is rathe...Fish morphological phenotypes are important resources in artificial breeding,functional gene mapping,and population-based studies in aquaculture and ecology.Traditional morphological measurement of phenotypes is rather expensive in terms of time and labor.More importantly,manual measurement is highly dependent on operational experience,which can lead to subjective phenotyping results.Here,we developed 3DPhenoFish software to extract fish morphological phenotypes from three-dimensional(3D)point cloud data.Algorithms for background elimination,coordinate normalization,image segmentation,key point recognition,and phenotype extraction were developed and integrated into an intuitive user interface.Furthermore,18 key points and traditional 2D morphological traits,along with 3D phenotypes,including area and volume,can be automatically obtained in a visualized manner.Intuitive fine-tuning of key points and customized definitions of phenotypes are also allowed in the software.Using 3DPhenoFish,we performed high-throughput phenotyping for four endemic Schizothoracinae species,including Schizopygopsis younghusbandi,Oxygymnocypris stewartii,Ptychobarbus dipogon,and Schizothorax oconnori.Results indicated that the morphological phenotypes from 3DPhenoFish exhibited high linear correlation(>0.94)with manual measurements and offered informative traits to discriminate samples of different species and even for different populations of the same species.In summary,we developed an efficient,accurate,and customizable tool,3DPhenoFish,to extract morphological phenotypes from point cloud data,which should help overcome traditional challenges in manual measurements.3DPhenoFish can be used for research on morphological phenotypes in fish,including functional gene mapping,artificial selection,and conservation studies.3DPhenoFish is an open-source software and can be downloaded for free at https://github.com/lyh24k/3DPhenoFish/tree/master.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
Point bars are well developed on the Yellow River delta, an~ which theShengli I point bar is the most typical. The point bar, being about 4 km in length and several tensto more than 100 meters in width, is located on ...Point bars are well developed on the Yellow River delta, an~ which theShengli I point bar is the most typical. The point bar, being about 4 km in length and several tensto more than 100 meters in width, is located on the south side of the Shengli Bridge in KenliCounty, Dongying, Shandong. It is a typical fine-grained point bar with silt, which is predominant,some clay and minor plant debris and clay boulders. The Shengli I Point bar has complicated 3-Dstructures. Firstly, in a plane view, it comprises mainly eight sedimentary units, bar edge, baredge, bar platform, bar plain, bar channel, bar gully, bar pond and bar bay, developing side by sideand superimposed one by one m a complex way. Secondly, its vertical structures are very complex dueto the partial superimposition of the 8 sedimentary units. Besides hydatogenesis, very intensivewind erosion, eolian, ice and meltwater actions are also visible on the Shengli I point bar. Thecomplex form is made even more complicated because of the above co-actions.展开更多
A new three-dimensional （3D） continuous autonomous system with one parameter and three quadratic terms is presented firstly in this paper. Countless embedded trumpet-shaped chaotic attractors in two opposite directi...A new three-dimensional （3D） continuous autonomous system with one parameter and three quadratic terms is presented firstly in this paper. Countless embedded trumpet-shaped chaotic attractors in two opposite directions are generated from the system as time goes on. The basic dynamical behaviors of the strange chaotic system are investigated. Another more complex 3D system with the same capability of generating countless embedded trumpet-shaped chaotic attractors is also put forward.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
Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)...Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)products.Combined with image data,this technology can further enrich and extract spatial geographic information.However,practically,due to the limited operating range of airborne LiDAR and the large area of task,it would be necessary to perform registration and stitching process on point clouds of adjacent flight strips.By eliminating grow errors,the systematic errors in the data need to be effectively reduced.Thus,this paper conducts research on point cloud registration methods in urban building areas,aiming to improve the accuracy and processing efficiency of airborne LiDAR data.Meanwhile,an improved post-ICP(Iterative Closest Point)point cloud registration method was proposed in this study to determine the accurate registration and efficient stitching of point clouds,which capable to provide a potential technical support for applicants in related field.展开更多
Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task.Firstly,because point cloud is formed by unstructured 3D points that makes the topology more compl...Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task.Firstly,because point cloud is formed by unstructured 3D points that makes the topology more complex.Secondly,the quality impairment generally involves both geometric attributes and color properties,where the measurement of the geometric distortion becomes more complex.We propose a perceptual point cloud quality assessment model that follows the perceptual features of Human Visual System(HVS)and the intrinsic characteristics of the point cloud.The point cloud is first pre-processed to extract the geometric skeleton keypoints with graph filtering-based re-sampling,and local neighboring regions around the geometric skeleton keypoints are constructed by K-Nearest Neighbors(KNN)clustering.For geometric distortion,the Point Feature Histogram(PFH)is extracted as the feature descriptor,and the Earth Mover’s Distance(EMD)between the PFHs of the corresponding local neighboring regions in the reference and the distorted point clouds is calculated as the geometric quality measurement.For color distortion,the statistical moments between the corresponding local neighboring regions are computed as the color quality measurement.Finally,the global perceptual quality assessment model is obtained as the linear weighting aggregation of the geometric and color quality measurement.The experimental results on extensive datasets show that the proposed method achieves the leading performance as compared to the state-of-the-art methods with less computing time.Meanwhile,the experimental results also demonstrate the robustness of the proposed method across various distortion types.The source codes are available at https://github.com/llsurreal919/Point Cloud Quality Assessment.展开更多
Mapping and analyzing rock mass discontinuities based on 3D(three-dimensional)point cloud(3DPC)is one of the most important work in the engineering geomechanical survey.To efficiently analyze the distribution of disco...Mapping and analyzing rock mass discontinuities based on 3D(three-dimensional)point cloud(3DPC)is one of the most important work in the engineering geomechanical survey.To efficiently analyze the distribution of discontinuities,a self-developed code termed as the cloud-group-cluster(CGC)method based on MATLAB for mapping and detecting discontinuities based on the 3DPC was introduced.The identification and optimization of discontinuity groups were performed using three key parameters,i.e.K,θ,and f.A sensitivity analysis approach for identifying the optimal key parameters was introduced.The results show that the comprehensive analysis of the main discontinuity groups,mean orientations,and densities could be achieved automatically.The accuracy of the CGC method was validated using tetrahedral and hexahedral models.The 3D point cloud data were divided into three levels(point cloud,group,and cluster)for analysis,and this three-level distribution recognition was applied to natural rock surfaces.The densities and spacing information of the principal discontinuities were automatically detected using the CGC method.Five engineering case studies were conducted to validate the CGC method,showing the applicability in detecting rock discontinuities based on 3DPC model.展开更多
Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmenta...Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.展开更多
The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,direc...The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.展开更多
Existing reverse-engineering methods struggle to directly generate editable,parametric CAD models from scanned data.To address this limitation,this paper proposes a reverse-modeling approach that reconstructs parametr...Existing reverse-engineering methods struggle to directly generate editable,parametric CAD models from scanned data.To address this limitation,this paper proposes a reverse-modeling approach that reconstructs parametric CAD models from multi-view RGB-D point clouds.Multi-frame point-cloud registration and fusion are first employed to obtain a complete 3-D point cloud of the target object.A region-growing algorithm that jointly exploits color and geometric information segments the cloud,while RANSAC robustly detects and fits basic geometric primitives.These primitives serve as nodes in a graph whose edge features are inferred by a graph neural network to capture spatial constraints.From the detected primitives and their constraints,a high-accuracy,fully editable parametric CAD model is finally exported.Experiments show an average parameter error of 0.3 mm for key dimensions and an overall geometric reconstruction accuracy of 0.35 mm.The work offers an effective technical route toward automated,intelligent 3-D reverse modeling.展开更多
Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft,and the requirement for mass data still means a high cost.To address this problem,we propose a novel point-cloud multi-condition...Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft,and the requirement for mass data still means a high cost.To address this problem,we propose a novel point-cloud multi-condition aerodynamics transfer learning(PCMCA-TL)framework that enables aerodynamic prediction in data-scarce sce-narios by transferring knowledge from well-learned scenarios.We modified the PointNeXt segmentation archi-tecture to a PointNeXtReg+regression model,including a working condition input module.The model is first pre-trained on a public dataset with 2000 shapes but only one working condition and then fine-tuned on a multi-condition small-scale spaceplane dataset.The effectiveness of the PCMCA-TL framework is verified by comparing the pressure coefficients predicted by direct training,pre-training,and TL models.Furthermore,by comparing the aerodynamic force coefficients calculated by predicted pressure coefficients in seconds with the correspond-ing CFD results obtained in hours,the accuracy highlights the development potential of deep transfer learning in aerodynamic evaluation.展开更多
Understanding the conformational characteristics of polymers is key to elucidating their physical properties.Cyclic polymers,defined by their closed-loop structures,inherently differ from linear polymers possessing di...Understanding the conformational characteristics of polymers is key to elucidating their physical properties.Cyclic polymers,defined by their closed-loop structures,inherently differ from linear polymers possessing distinct chain ends.Despite these structural differences,both types of polymers exhibit locally random-walk-like conformations,making it challenging to detect subtle spatial variations using conventional methods.In this study,we address this challenge by integrating molecular dynamics simulations with point cloud neural networks to analyze the spatial conformations of cyclic and linear polymers.By utilizing the Dynamic Graph CNN(DGCNN)model,we classify polymer conformations based on the 3D coordinates of monomers,capturing local and global topological differences without considering chain connectivity sequentiality.Our findings reveal that the optimal local structural feature unit size scales linearly with molecular weight,aligning with theoretical predictions.Additionally,interpretability techniques such as Grad-CAM and SHAP identify significant conformational differences:cyclic polymers tend to form prolate ellipsoid shapes with pronounced elongation along the major axis,while linear polymers show elongated ends with more spherical centers.These findings reveal subtle yet critical differences in local conformations between cyclic and linear polymers that were previously difficult to discern,providing deeper insights into polymer structure-property relationships and offering guidance for future polymer science advancements.展开更多
Few-shot point cloud 3D object detection(FS3D)aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the nove...Few-shot point cloud 3D object detection(FS3D)aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes.Due to imbalanced training data,existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes,which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects.To address these issues,this thesis proposes a category-agnostic contrastive learning approach,enhancing the generalization and identification abilities for almost unseen categories through the construction of pseudo-labels and positive-negative sample pairs unrelated to specific classes.Firstly,this thesis designs a proposal-wise context contrastive module(CCM).By reducing the distance between foreground point features and increasing the distance between foreground and background point features within a region proposal,CCM aids the network in extracting more discriminative foreground and background feature representations without reliance on categorical annotations.Secondly,this thesis utilizes a geometric contrastive module(GCM),which enhances the network’s geometric perception capability by employing contrastive learning on the foreground point features associated with various basic geometric components,such as edges,corners,and surfaces,thereby enabling these geometric components to exhibit more distinguishable representations.This thesis also combines category-aware contrastive learning with former modules to maintain categorical distinctiveness.Extensive experimental results on FS-SUNRGBD and FS-ScanNet datasets demonstrate the effectiveness of this method with average precision exceeding the baseline by up to 8%.展开更多
This study presents a framework for the semi-automatic detection of rock discontinuities using a threedimensional(3D)point cloud(PC).The process begins by selecting an appropriate neighborhood size,a critical step for...This study presents a framework for the semi-automatic detection of rock discontinuities using a threedimensional(3D)point cloud(PC).The process begins by selecting an appropriate neighborhood size,a critical step for feature extraction from the PC.The effects of different neighborhood sizes(k=5,10,20,50,and 100)have been evaluated to assess their impact on classification performance.After that,17 geometric and spatial features were extracted from the PC.Next,ensemble methods,AdaBoost.M2,random forest,and decision tree,have been compared with Artificial Neural Networks to classify the main discontinuity sets.The McNemar test indicates that the classifiers are statistically significant.The random forest classifier consistently achieves the highest performance with an accuracy exceeding 95%when using a neighborhood size of k=100,while recall,F-score,and Cohen's Kappa also demonstrate high success.SHapley Additive exPlanations(SHAP),an Explainable AI technique,has been used to evaluate feature importance and improve the explainability of black-box machine learning models in the context of rock discontinuity classification.The analysis reveals that features such as normal vectors,verticality,and Z-values have the greatest influence on identifying main discontinuity sets,while linearity,planarity,and eigenvalues contribute less,making the model more transparent and easier to understand.After classification,individual discontinuity sets were detected using a revised DBSCAN from the main discontinuity sets.Finally,the orientation parameters of the plane fitted to each discontinuity were derived from the plane parameters obtained using the Random Sample Consensus(RANSAC).Two real-world datasets(obtained from SfM and LiDAR)and one synthetic dataset were used to validate the proposed method,which successfully identified rock discontinuities and their orientation parameters(dip angle/direction).展开更多
In the task of inspecting underwater suspended pipelines,multi-beam sonar(MBS)can provide two-dimensional water column images(WCIs).However,systematic interferences(e.g.,sidelobe effects)may induce misdetection in WCI...In the task of inspecting underwater suspended pipelines,multi-beam sonar(MBS)can provide two-dimensional water column images(WCIs).However,systematic interferences(e.g.,sidelobe effects)may induce misdetection in WCIs.To address this issue and improve the accuracy of detection,we developed a density-based clustering method for three-dimensional water column point clouds.During the processing of WCIs,sidelobe effects are mitigated using a bilateral filter and brightness transformation.The cross-sectional point cloud of the pipeline is then extracted by using the Canny operator.In the detection phase,the target is identified by using density-based spatial clustering of applications with noise(DBSCAN).However,the selection of appropriate DBSCAN parameters is obscured by the uneven distribution of the water column point cloud.To overcome this,we propose an improved DBSCAN based on a parameter interval estimation method(PIE-DBSCAN).First,kernel density estimation(KDE)is used to determine the candidate interval of parameters,after which the exact cluster number is determined via density peak clustering(DPC).Finally,the optimal parameters are selected by comparing the mean silhouette coefficients.To validate the performance of PIE-DBSCAN,we collected water column point clouds from an anechoic tank and the South China Sea.PIE-DBSCAN successfully detected both the target points of the suspended pipeline and non-target points on the seafloor surface.Compared to the K-Means and Mean-Shift algorithms,PIE-DBSCAN demonstrates superior clustering performance and shows feasibility in practical applications.展开更多
The identification of rock mass discontinuities is critical for rock mass characterization.While high-resolution digital outcrop models(DOMs)are widely used,current digital methods struggle to generalize across divers...The identification of rock mass discontinuities is critical for rock mass characterization.While high-resolution digital outcrop models(DOMs)are widely used,current digital methods struggle to generalize across diverse geological settings.Large-scale models(LSMs),with vast parameter spaces and extensive training datasets,excel in solving complex visual problems.This study explores the potential of using one such LSM,Segment anything model(SAM),to identify facet-type discontinuities across several outcrops via interactive prompting.The findings demonstrate that SAM effectively segments two-dimensional(2D)discontinuities,with its generalization capability validated on a dataset of 2426 identified discontinuities across 170 outcrops.The model achieves 0.78 mean IoU and 0.86 average precision using 11-point prompts.To extend to three dimensions(3D),a framework integrating SAM with Structure-from-Motion(SfM)was proposed.By utilizing the inherent but often overlooked relationship between image pixels and point clouds in SfM,the identification process was simplified and generalized across photogrammetric devices.Benchmark studies showed that the framework achieved 0.91 average precision,identifying 87 discontinuities in Dataset-3D.The results confirm its high precision and efficiency,making it a valuable tool for data annotation.The proposed method offers a practical solution for geological investigations.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supported by the National Natural Science Foundation of China(32072980)Key Research and Development Projects in Tibet(XZ202001ZY0016N,XZ201902NB02,XZNKY-2019-C-053)。
文摘Fish morphological phenotypes are important resources in artificial breeding,functional gene mapping,and population-based studies in aquaculture and ecology.Traditional morphological measurement of phenotypes is rather expensive in terms of time and labor.More importantly,manual measurement is highly dependent on operational experience,which can lead to subjective phenotyping results.Here,we developed 3DPhenoFish software to extract fish morphological phenotypes from three-dimensional(3D)point cloud data.Algorithms for background elimination,coordinate normalization,image segmentation,key point recognition,and phenotype extraction were developed and integrated into an intuitive user interface.Furthermore,18 key points and traditional 2D morphological traits,along with 3D phenotypes,including area and volume,can be automatically obtained in a visualized manner.Intuitive fine-tuning of key points and customized definitions of phenotypes are also allowed in the software.Using 3DPhenoFish,we performed high-throughput phenotyping for four endemic Schizothoracinae species,including Schizopygopsis younghusbandi,Oxygymnocypris stewartii,Ptychobarbus dipogon,and Schizothorax oconnori.Results indicated that the morphological phenotypes from 3DPhenoFish exhibited high linear correlation(>0.94)with manual measurements and offered informative traits to discriminate samples of different species and even for different populations of the same species.In summary,we developed an efficient,accurate,and customizable tool,3DPhenoFish,to extract morphological phenotypes from point cloud data,which should help overcome traditional challenges in manual measurements.3DPhenoFish can be used for research on morphological phenotypes in fish,including functional gene mapping,artificial selection,and conservation studies.3DPhenoFish is an open-source software and can be downloaded for free at https://github.com/lyh24k/3DPhenoFish/tree/master.
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.
文摘Point bars are well developed on the Yellow River delta, an~ which theShengli I point bar is the most typical. The point bar, being about 4 km in length and several tensto more than 100 meters in width, is located on the south side of the Shengli Bridge in KenliCounty, Dongying, Shandong. It is a typical fine-grained point bar with silt, which is predominant,some clay and minor plant debris and clay boulders. The Shengli I Point bar has complicated 3-Dstructures. Firstly, in a plane view, it comprises mainly eight sedimentary units, bar edge, baredge, bar platform, bar plain, bar channel, bar gully, bar pond and bar bay, developing side by sideand superimposed one by one m a complex way. Secondly, its vertical structures are very complex dueto the partial superimposition of the 8 sedimentary units. Besides hydatogenesis, very intensivewind erosion, eolian, ice and meltwater actions are also visible on the Shengli I point bar. Thecomplex form is made even more complicated because of the above co-actions.
基金supported by the Science Research Foundation of Liaoning Provincial Education Department,China(Grant No.L2013229)
文摘A new three-dimensional （3D） continuous autonomous system with one parameter and three quadratic terms is presented firstly in this paper. Countless embedded trumpet-shaped chaotic attractors in two opposite directions are generated from the system as time goes on. The basic dynamical behaviors of the strange chaotic system are investigated. Another more complex 3D system with the same capability of generating countless embedded trumpet-shaped chaotic attractors is also put forward.
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Guangxi Key Laboratory of Spatial Information and Geomatics(21-238-21-12)Guangxi Young and Middle-aged Teachers’Research Fundamental Ability Enhancement Project(2023KY1196).
文摘Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)products.Combined with image data,this technology can further enrich and extract spatial geographic information.However,practically,due to the limited operating range of airborne LiDAR and the large area of task,it would be necessary to perform registration and stitching process on point clouds of adjacent flight strips.By eliminating grow errors,the systematic errors in the data need to be effectively reduced.Thus,this paper conducts research on point cloud registration methods in urban building areas,aiming to improve the accuracy and processing efficiency of airborne LiDAR data.Meanwhile,an improved post-ICP(Iterative Closest Point)point cloud registration method was proposed in this study to determine the accurate registration and efficient stitching of point clouds,which capable to provide a potential technical support for applicants in related field.
基金supported in part by the National Natural Science Foundation of China under Grant(62171257,U22B2001,U19A2052,62020106011,62061015)in part by the Natural Science Foundation of Chongqing under Grant(2023NSCQMSX2930)+1 种基金in part by the Youth Innovation Group Support Program of ICE Discipline of CQUPT under Grant(SCIE-QN-2022-05)in part by the Graduate Scientifc Research and Innovation Project of Chongqing under Grant(CYS22469).
文摘Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task.Firstly,because point cloud is formed by unstructured 3D points that makes the topology more complex.Secondly,the quality impairment generally involves both geometric attributes and color properties,where the measurement of the geometric distortion becomes more complex.We propose a perceptual point cloud quality assessment model that follows the perceptual features of Human Visual System(HVS)and the intrinsic characteristics of the point cloud.The point cloud is first pre-processed to extract the geometric skeleton keypoints with graph filtering-based re-sampling,and local neighboring regions around the geometric skeleton keypoints are constructed by K-Nearest Neighbors(KNN)clustering.For geometric distortion,the Point Feature Histogram(PFH)is extracted as the feature descriptor,and the Earth Mover’s Distance(EMD)between the PFHs of the corresponding local neighboring regions in the reference and the distorted point clouds is calculated as the geometric quality measurement.For color distortion,the statistical moments between the corresponding local neighboring regions are computed as the color quality measurement.Finally,the global perceptual quality assessment model is obtained as the linear weighting aggregation of the geometric and color quality measurement.The experimental results on extensive datasets show that the proposed method achieves the leading performance as compared to the state-of-the-art methods with less computing time.Meanwhile,the experimental results also demonstrate the robustness of the proposed method across various distortion types.The source codes are available at https://github.com/llsurreal919/Point Cloud Quality Assessment.
基金supported by the National Key Research and Development Program of China(Grant Nos.2023YFC2907400 and 2021YFC2900500)the National Natural Science Foundation of China(Grant No.52074020).
文摘Mapping and analyzing rock mass discontinuities based on 3D(three-dimensional)point cloud(3DPC)is one of the most important work in the engineering geomechanical survey.To efficiently analyze the distribution of discontinuities,a self-developed code termed as the cloud-group-cluster(CGC)method based on MATLAB for mapping and detecting discontinuities based on the 3DPC was introduced.The identification and optimization of discontinuity groups were performed using three key parameters,i.e.K,θ,and f.A sensitivity analysis approach for identifying the optimal key parameters was introduced.The results show that the comprehensive analysis of the main discontinuity groups,mean orientations,and densities could be achieved automatically.The accuracy of the CGC method was validated using tetrahedral and hexahedral models.The 3D point cloud data were divided into three levels(point cloud,group,and cluster)for analysis,and this three-level distribution recognition was applied to natural rock surfaces.The densities and spacing information of the principal discontinuities were automatically detected using the CGC method.Five engineering case studies were conducted to validate the CGC method,showing the applicability in detecting rock discontinuities based on 3DPC model.
基金Postgraduate Innovation Top notch Talent Training Project of Hunan Province,Grant/Award Number:CX20220045Scientific Research Project of National University of Defense Technology,Grant/Award Number:22-ZZCX-07+2 种基金New Era Education Quality Project of Anhui Province,Grant/Award Number:2023cxcysj194National Natural Science Foundation of China,Grant/Award Numbers:62201597,62205372,1210456foundation of Hefei Comprehensive National Science Center,Grant/Award Number:KY23C502。
文摘Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.
基金National Key Research and Development Program of China,Grant/Award Number:2020YFB1711704。
文摘The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.
文摘Existing reverse-engineering methods struggle to directly generate editable,parametric CAD models from scanned data.To address this limitation,this paper proposes a reverse-modeling approach that reconstructs parametric CAD models from multi-view RGB-D point clouds.Multi-frame point-cloud registration and fusion are first employed to obtain a complete 3-D point cloud of the target object.A region-growing algorithm that jointly exploits color and geometric information segments the cloud,while RANSAC robustly detects and fits basic geometric primitives.These primitives serve as nodes in a graph whose edge features are inferred by a graph neural network to capture spatial constraints.From the detected primitives and their constraints,a high-accuracy,fully editable parametric CAD model is finally exported.Experiments show an average parameter error of 0.3 mm for key dimensions and an overall geometric reconstruction accuracy of 0.35 mm.The work offers an effective technical route toward automated,intelligent 3-D reverse modeling.
基金supported by the Natural Science Foundation of Hunan Province of China(Grant No.2021JJ10045).
文摘Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft,and the requirement for mass data still means a high cost.To address this problem,we propose a novel point-cloud multi-condition aerodynamics transfer learning(PCMCA-TL)framework that enables aerodynamic prediction in data-scarce sce-narios by transferring knowledge from well-learned scenarios.We modified the PointNeXt segmentation archi-tecture to a PointNeXtReg+regression model,including a working condition input module.The model is first pre-trained on a public dataset with 2000 shapes but only one working condition and then fine-tuned on a multi-condition small-scale spaceplane dataset.The effectiveness of the PCMCA-TL framework is verified by comparing the pressure coefficients predicted by direct training,pre-training,and TL models.Furthermore,by comparing the aerodynamic force coefficients calculated by predicted pressure coefficients in seconds with the correspond-ing CFD results obtained in hours,the accuracy highlights the development potential of deep transfer learning in aerodynamic evaluation.
基金the National Key R&D Program of China(No.2022YFB3707303)National Natural Science Foundation of China(No.52293471)。
文摘Understanding the conformational characteristics of polymers is key to elucidating their physical properties.Cyclic polymers,defined by their closed-loop structures,inherently differ from linear polymers possessing distinct chain ends.Despite these structural differences,both types of polymers exhibit locally random-walk-like conformations,making it challenging to detect subtle spatial variations using conventional methods.In this study,we address this challenge by integrating molecular dynamics simulations with point cloud neural networks to analyze the spatial conformations of cyclic and linear polymers.By utilizing the Dynamic Graph CNN(DGCNN)model,we classify polymer conformations based on the 3D coordinates of monomers,capturing local and global topological differences without considering chain connectivity sequentiality.Our findings reveal that the optimal local structural feature unit size scales linearly with molecular weight,aligning with theoretical predictions.Additionally,interpretability techniques such as Grad-CAM and SHAP identify significant conformational differences:cyclic polymers tend to form prolate ellipsoid shapes with pronounced elongation along the major axis,while linear polymers show elongated ends with more spherical centers.These findings reveal subtle yet critical differences in local conformations between cyclic and linear polymers that were previously difficult to discern,providing deeper insights into polymer structure-property relationships and offering guidance for future polymer science advancements.
文摘Few-shot point cloud 3D object detection(FS3D)aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes.Due to imbalanced training data,existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes,which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects.To address these issues,this thesis proposes a category-agnostic contrastive learning approach,enhancing the generalization and identification abilities for almost unseen categories through the construction of pseudo-labels and positive-negative sample pairs unrelated to specific classes.Firstly,this thesis designs a proposal-wise context contrastive module(CCM).By reducing the distance between foreground point features and increasing the distance between foreground and background point features within a region proposal,CCM aids the network in extracting more discriminative foreground and background feature representations without reliance on categorical annotations.Secondly,this thesis utilizes a geometric contrastive module(GCM),which enhances the network’s geometric perception capability by employing contrastive learning on the foreground point features associated with various basic geometric components,such as edges,corners,and surfaces,thereby enabling these geometric components to exhibit more distinguishable representations.This thesis also combines category-aware contrastive learning with former modules to maintain categorical distinctiveness.Extensive experimental results on FS-SUNRGBD and FS-ScanNet datasets demonstrate the effectiveness of this method with average precision exceeding the baseline by up to 8%.
文摘This study presents a framework for the semi-automatic detection of rock discontinuities using a threedimensional(3D)point cloud(PC).The process begins by selecting an appropriate neighborhood size,a critical step for feature extraction from the PC.The effects of different neighborhood sizes(k=5,10,20,50,and 100)have been evaluated to assess their impact on classification performance.After that,17 geometric and spatial features were extracted from the PC.Next,ensemble methods,AdaBoost.M2,random forest,and decision tree,have been compared with Artificial Neural Networks to classify the main discontinuity sets.The McNemar test indicates that the classifiers are statistically significant.The random forest classifier consistently achieves the highest performance with an accuracy exceeding 95%when using a neighborhood size of k=100,while recall,F-score,and Cohen's Kappa also demonstrate high success.SHapley Additive exPlanations(SHAP),an Explainable AI technique,has been used to evaluate feature importance and improve the explainability of black-box machine learning models in the context of rock discontinuity classification.The analysis reveals that features such as normal vectors,verticality,and Z-values have the greatest influence on identifying main discontinuity sets,while linearity,planarity,and eigenvalues contribute less,making the model more transparent and easier to understand.After classification,individual discontinuity sets were detected using a revised DBSCAN from the main discontinuity sets.Finally,the orientation parameters of the plane fitted to each discontinuity were derived from the plane parameters obtained using the Random Sample Consensus(RANSAC).Two real-world datasets(obtained from SfM and LiDAR)and one synthetic dataset were used to validate the proposed method,which successfully identified rock discontinuities and their orientation parameters(dip angle/direction).
基金the National Natural Science Foundation of China(Nos.42176188,42176192)the Hainan Provincial Natural Science Foundation of China(No.421CXTD442)+2 种基金the Stable Supporting Fund of Acoustic Science and Technology Laboratory(No.JCKYS2024604SSJS007)the Fundamental Research Funds for the Central Universities(No.3072024CFJ0504)the Harbin Engineering University Doctoral Research and Innovation Fund(No.XK2050021034)。
文摘In the task of inspecting underwater suspended pipelines,multi-beam sonar(MBS)can provide two-dimensional water column images(WCIs).However,systematic interferences(e.g.,sidelobe effects)may induce misdetection in WCIs.To address this issue and improve the accuracy of detection,we developed a density-based clustering method for three-dimensional water column point clouds.During the processing of WCIs,sidelobe effects are mitigated using a bilateral filter and brightness transformation.The cross-sectional point cloud of the pipeline is then extracted by using the Canny operator.In the detection phase,the target is identified by using density-based spatial clustering of applications with noise(DBSCAN).However,the selection of appropriate DBSCAN parameters is obscured by the uneven distribution of the water column point cloud.To overcome this,we propose an improved DBSCAN based on a parameter interval estimation method(PIE-DBSCAN).First,kernel density estimation(KDE)is used to determine the candidate interval of parameters,after which the exact cluster number is determined via density peak clustering(DPC).Finally,the optimal parameters are selected by comparing the mean silhouette coefficients.To validate the performance of PIE-DBSCAN,we collected water column point clouds from an anechoic tank and the South China Sea.PIE-DBSCAN successfully detected both the target points of the suspended pipeline and non-target points on the seafloor surface.Compared to the K-Means and Mean-Shift algorithms,PIE-DBSCAN demonstrates superior clustering performance and shows feasibility in practical applications.
基金support in dataset preparation.This study was funded by National Natural Science Foundation of China(Nos.42422704 and 52379109)Opening the fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Chengdu University of Technology)(No.SKLGP2024K028)Science and Technology Research and Design Projects of China State Construction Engineering Corporation Ltd.(No.CSCEC-2024-Q-68).
文摘The identification of rock mass discontinuities is critical for rock mass characterization.While high-resolution digital outcrop models(DOMs)are widely used,current digital methods struggle to generalize across diverse geological settings.Large-scale models(LSMs),with vast parameter spaces and extensive training datasets,excel in solving complex visual problems.This study explores the potential of using one such LSM,Segment anything model(SAM),to identify facet-type discontinuities across several outcrops via interactive prompting.The findings demonstrate that SAM effectively segments two-dimensional(2D)discontinuities,with its generalization capability validated on a dataset of 2426 identified discontinuities across 170 outcrops.The model achieves 0.78 mean IoU and 0.86 average precision using 11-point prompts.To extend to three dimensions(3D),a framework integrating SAM with Structure-from-Motion(SfM)was proposed.By utilizing the inherent but often overlooked relationship between image pixels and point clouds in SfM,the identification process was simplified and generalized across photogrammetric devices.Benchmark studies showed that the framework achieved 0.91 average precision,identifying 87 discontinuities in Dataset-3D.The results confirm its high precision and efficiency,making it a valuable tool for data annotation.The proposed method offers a practical solution for geological investigations.
