Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,su...Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,suffer from accuracy degradation,omission of critical discontinuities when orientation density is unevenly distributed,and need manual intervention.To overcome these limitations,this paper introduces a novel discontinuities identificationmethod based on geometric feature analysis of rock mass.By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm,the method accurately detects independent discontinuities under complex geological conditions.Given that rock mass orientations typically follow a Fisher distribution,an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets,eliminating the need for preset clusters or thresholds inherent in traditional methods.The proposed approach effectively handles diverse rock mass shapes and sizes,leveraging both local and global geometric features to minimize noise interference.Experimental validation on three real-world rock mass models,alongside comparisons with three conventional directional clustering algorithms,demonstrates superior accuracy and robustness in identifying optimal discontinuity sets.The proposed method offers a reliable and efficienttool for discontinuities detection and grouping in underground engineering,significantlyenhancing design and construction outcomes.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
To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determ...To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determining the position of the grasping information.These results can then be used to guide the visually impaired or to execute grasping tasks with a robotic arm.In this paper,we collected,annotated,and published the benchmark TQUGraspingObject dataset for testing,validation,and evaluation of deep learning(DL)models for detecting,recognizing,and localizing grasping objects in 2D and 3D space,especially 3D point cloud data.Our dataset is collected in a shared room,with common everyday objects placed on the tabletop in jumbled positions by Intel RealSense D435(IR-D435).This dataset includes more than 63k RGB-D pairs and related data such as normalized 3D object point cloud,3D object point cloud segmented,coordinate system normalizationmatrix,3D object point cloud normalized,and hand pose for grasping each object.At the same time,we also conducted experiments on fourDL networks with the best performance:SSD-MobileNetV3,ResNet50-Transformer,ResNet101-Transformer,and YOLOv12.The results present that YOLOv12 has the most suitable results in detecting and recognizing objects in images.All data,annotations,toolkit,source code,point cloud data,and results are publicly available on our project website:https://github.com/HuaTThanhIT2327Tqu/datasetv2.展开更多
海冰是海洋环境监测的重点对象,光学遥感能为海冰的精细化监测提供技术支持,实现海冰的动态监测与量化估算。中国海洋水色业务卫星星座——海洋一号C/D卫星(Haiyang-1C/D,以下简称HY-1C/D),搭载有适用于海冰监测的海岸带成像仪CZI (Coas...海冰是海洋环境监测的重点对象,光学遥感能为海冰的精细化监测提供技术支持,实现海冰的动态监测与量化估算。中国海洋水色业务卫星星座——海洋一号C/D卫星(Haiyang-1C/D,以下简称HY-1C/D),搭载有适用于海冰监测的海岸带成像仪CZI (Coastal Zone Imager)和水色水温扫描仪COCTS (Chinese Ocean Color and Temperature Scanner),具备开展海冰业务化监测应用的能力。本研究以2021年12月—2022年3月中国渤海辽东湾海冰为研究区,收集冰期内的HY-1C/D卫星影像数据,开展海冰识别与估算研究,评估CZI与COCTS数据对海冰的识别效能,分析海冰、海水、云等典型目标在光学(可见光—近红外)和热红外波段的影像特征;此外,针对光学遥感影像中海冰识别易受到云干扰问题,根据其在以上波段的遥感响应机理和图像特征差异,提出一种适用于HY-1C/D卫星在海冰分布区域的云掩膜方法,并对海冰进行精确提取;在识别基础上,进一步评估HY-1C/D卫星数据对于海冰密集度这一关键物理参数的光学遥感估算效能。结果表明:引入热红外波段,利用海冰和云的亮温差异对云进行掩膜,使得利用全局阈值提取海冰像元成为可能;通过对CZI、COCTS影像中海冰的高精度提取,进一步实现海冰密集度的估算,有效反映了像元中海冰和海水的混合程度,从而达到海冰海水像元解混的效果,提高海冰覆盖面积的估算精度。综上,本研究方法针对HY-1C/D卫星影像数据中的海冰识别提取具有较高的精度和抗干扰能力,可为国产海洋光学卫星的海冰监测业务化应用提供方法参考。展开更多
The centroid coordinate serves as a critical control parameter in motion systems,including aircraft,missiles,rockets,and drones,directly influencing their motion dynamics and control performance.Traditional methods fo...The centroid coordinate serves as a critical control parameter in motion systems,including aircraft,missiles,rockets,and drones,directly influencing their motion dynamics and control performance.Traditional methods for centroid measurement often necessitate custom equipment and specialized positioning devices,leading to high costs and limited accuracy.Here,we present a centroid measurement method that integrates 3D scanning technology,enabling accurate measurement of centroid across various types of objects without the need for specialized positioning fixtures.A theoretical framework for centroid measurement was established,which combined the principle of the multi-point weighing method with 3D scanning technology.The measurement accuracy was evaluated using a designed standard component.Experimental results demonstrate that the discrepancies between the theoretical and the measured centroid of a standard component with various materials and complex shapes in the X,Y,and Z directions are 0.003 mm,0.009 mm,and 0.105 mm,respectively,yielding a spatial deviation of 0.106 mm.Qualitative verification was conducted through experimental validation of three distinct types.They confirmed the reliability of the proposed method,which allowed for accurate centroid measurements of various products without requiring positioning fixtures.This advancement significantly broadened the applicability and scope of centroid measurement devices,offering new theoretical insights and methodologies for the measurement of complex parts and systems.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
Efficient three-dimensional(3D)building reconstruction from drone imagery often faces data acquisition,storage,and computational challenges because of its reliance on dense point clouds.In this study,we introduced a n...Efficient three-dimensional(3D)building reconstruction from drone imagery often faces data acquisition,storage,and computational challenges because of its reliance on dense point clouds.In this study,we introduced a novel method for efficient and lightweight 3D building reconstruction from drone imagery using line clouds and sparse point clouds.Our approach eliminates the need to generate dense point clouds,and thus significantly reduces the computational burden by reconstructing 3D models directly from sparse data.We addressed the limitations of line clouds for plane detection and reconstruction by using a new algorithm.This algorithm projects 3D line clouds onto a 2D plane,clusters the projections to identify potential planes,and refines them using sparse point clouds to ensure an accurate and efficient model reconstruction.Extensive qualitative and quantitative experiments demonstrated the effectiveness of our method,demonstrating its superiority over existing techniques in terms of simplicity and efficiency.展开更多
Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmenta...Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmentation techniques for point clouds,where global augmentation is applied to the entire point cloud of the scene,and cut-paste samples objects from other frames into the current frame.Both types of data augmentation can improve performance,but the cut-paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling,which may be counterproductive and may hurt the overall performance.In addition,LiDAR is susceptible to signal loss,external occlusion,extreme weather and other factors,which can easily cause object shape changes,while global augmentation and cut-paste cannot effectively enhance the robustness of the model.To this end,we propose Syn-Aug,a synchronous data augmentation framework for LiDAR-based 3D object detection.Specifically,we first propose a novel rendering-based object augmentation technique(Ren-Aug)to enrich training data while enhancing scene realism.Second,we propose a local augmentation technique(Local-Aug)to generate local noise by rotating and scaling objects in the scene while avoiding collisions,which can improve generalisation performance.Finally,we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames.We verify the proposed framework with four different types of 3D object detectors.Experimental results show that our proposed Syn-Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets,proving the effectiveness and generality of Syn-Aug.On KITTI,four different types of baseline models using Syn-Aug improved mAP by 0.89%,1.35%,1.61%and 1.14%respectively.On nuScenes,four different types of baseline models using Syn-Aug improved mAP by 14.93%,10.42%,8.47%and 6.81%respectively.The code is available at https://github.com/liuhuaijjin/Syn-Aug.展开更多
With the widespread application of 3D visualization in digital exhibition halls and virtual reality,achieving efficient rendering and high-fidelity presentation has become a key challenge.This study proposes a hybrid ...With the widespread application of 3D visualization in digital exhibition halls and virtual reality,achieving efficient rendering and high-fidelity presentation has become a key challenge.This study proposes a hybrid point cloud generation method that combines traditional sampling with 3D Gaussian splatting,aiming to address the issues of rendering delay and missing details in existing 3D displays.By improving the OBJ model parsing process and incorporating an adaptive area-weighted sampling algorithm,we achieve adaptive point cloud generation based on triangle density.Innovatively,we advance the ellipsoidal parameter estimation process of 3D Gaussian splatting to the point cloud generation stage.By establishing a mathematical relationship between the covariance matrix and local curvature,the generated point cloud naturally exhibits Gaussian distribution characteristics.Experimental results show that,compared to traditional methods,our approach reduces point cloud data by 38% while maintaining equivalent visual quality at a 4096×4096 texture resolution.By introducing mipmap texture optimization strategies and a GPU-accelerated rasterization pipeline,stable rendering at 60 frames per second is achieved in a WebGL environment.Additionally,we quantize and compress the spherical harmonic function parameters specific to 3D Gaussian splatting,reducing network transmission bandwidth to 52% of the original data.This study provides a new technical pathway for fields requiring high-precision display,such as the digitization of cultural heritage.展开更多
基金the National Key Research and Development Program of China(Grant No.2023YFC3009400).
文摘Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,suffer from accuracy degradation,omission of critical discontinuities when orientation density is unevenly distributed,and need manual intervention.To overcome these limitations,this paper introduces a novel discontinuities identificationmethod based on geometric feature analysis of rock mass.By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm,the method accurately detects independent discontinuities under complex geological conditions.Given that rock mass orientations typically follow a Fisher distribution,an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets,eliminating the need for preset clusters or thresholds inherent in traditional methods.The proposed approach effectively handles diverse rock mass shapes and sizes,leveraging both local and global geometric features to minimize noise interference.Experimental validation on three real-world rock mass models,alongside comparisons with three conventional directional clustering algorithms,demonstrates superior accuracy and robustness in identifying optimal discontinuity sets.The proposed method offers a reliable and efficienttool for discontinuities detection and grouping in underground engineering,significantlyenhancing design and construction outcomes.
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.
文摘To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determining the position of the grasping information.These results can then be used to guide the visually impaired or to execute grasping tasks with a robotic arm.In this paper,we collected,annotated,and published the benchmark TQUGraspingObject dataset for testing,validation,and evaluation of deep learning(DL)models for detecting,recognizing,and localizing grasping objects in 2D and 3D space,especially 3D point cloud data.Our dataset is collected in a shared room,with common everyday objects placed on the tabletop in jumbled positions by Intel RealSense D435(IR-D435).This dataset includes more than 63k RGB-D pairs and related data such as normalized 3D object point cloud,3D object point cloud segmented,coordinate system normalizationmatrix,3D object point cloud normalized,and hand pose for grasping each object.At the same time,we also conducted experiments on fourDL networks with the best performance:SSD-MobileNetV3,ResNet50-Transformer,ResNet101-Transformer,and YOLOv12.The results present that YOLOv12 has the most suitable results in detecting and recognizing objects in images.All data,annotations,toolkit,source code,point cloud data,and results are publicly available on our project website:https://github.com/HuaTThanhIT2327Tqu/datasetv2.
文摘海冰是海洋环境监测的重点对象,光学遥感能为海冰的精细化监测提供技术支持,实现海冰的动态监测与量化估算。中国海洋水色业务卫星星座——海洋一号C/D卫星(Haiyang-1C/D,以下简称HY-1C/D),搭载有适用于海冰监测的海岸带成像仪CZI (Coastal Zone Imager)和水色水温扫描仪COCTS (Chinese Ocean Color and Temperature Scanner),具备开展海冰业务化监测应用的能力。本研究以2021年12月—2022年3月中国渤海辽东湾海冰为研究区,收集冰期内的HY-1C/D卫星影像数据,开展海冰识别与估算研究,评估CZI与COCTS数据对海冰的识别效能,分析海冰、海水、云等典型目标在光学(可见光—近红外)和热红外波段的影像特征;此外,针对光学遥感影像中海冰识别易受到云干扰问题,根据其在以上波段的遥感响应机理和图像特征差异,提出一种适用于HY-1C/D卫星在海冰分布区域的云掩膜方法,并对海冰进行精确提取;在识别基础上,进一步评估HY-1C/D卫星数据对于海冰密集度这一关键物理参数的光学遥感估算效能。结果表明:引入热红外波段,利用海冰和云的亮温差异对云进行掩膜,使得利用全局阈值提取海冰像元成为可能;通过对CZI、COCTS影像中海冰的高精度提取,进一步实现海冰密集度的估算,有效反映了像元中海冰和海水的混合程度,从而达到海冰海水像元解混的效果,提高海冰覆盖面积的估算精度。综上,本研究方法针对HY-1C/D卫星影像数据中的海冰识别提取具有较高的精度和抗干扰能力,可为国产海洋光学卫星的海冰监测业务化应用提供方法参考。
基金supported by National Natural Science Foundation of China(No.52176122).
文摘The centroid coordinate serves as a critical control parameter in motion systems,including aircraft,missiles,rockets,and drones,directly influencing their motion dynamics and control performance.Traditional methods for centroid measurement often necessitate custom equipment and specialized positioning devices,leading to high costs and limited accuracy.Here,we present a centroid measurement method that integrates 3D scanning technology,enabling accurate measurement of centroid across various types of objects without the need for specialized positioning fixtures.A theoretical framework for centroid measurement was established,which combined the principle of the multi-point weighing method with 3D scanning technology.The measurement accuracy was evaluated using a designed standard component.Experimental results demonstrate that the discrepancies between the theoretical and the measured centroid of a standard component with various materials and complex shapes in the X,Y,and Z directions are 0.003 mm,0.009 mm,and 0.105 mm,respectively,yielding a spatial deviation of 0.106 mm.Qualitative verification was conducted through experimental validation of three distinct types.They confirmed the reliability of the proposed method,which allowed for accurate centroid measurements of various products without requiring positioning fixtures.This advancement significantly broadened the applicability and scope of centroid measurement devices,offering new theoretical insights and methodologies for the measurement of complex parts and systems.
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Supported by the Guangdong Major Project of Basic and Applied Basic Research (2023B0303000016)the National Natural Science Foundation of China (U21A20515)。
文摘Efficient three-dimensional(3D)building reconstruction from drone imagery often faces data acquisition,storage,and computational challenges because of its reliance on dense point clouds.In this study,we introduced a novel method for efficient and lightweight 3D building reconstruction from drone imagery using line clouds and sparse point clouds.Our approach eliminates the need to generate dense point clouds,and thus significantly reduces the computational burden by reconstructing 3D models directly from sparse data.We addressed the limitations of line clouds for plane detection and reconstruction by using a new algorithm.This algorithm projects 3D line clouds onto a 2D plane,clusters the projections to identify potential planes,and refines them using sparse point clouds to ensure an accurate and efficient model reconstruction.Extensive qualitative and quantitative experiments demonstrated the effectiveness of our method,demonstrating its superiority over existing techniques in terms of simplicity and efficiency.
基金supported by National Natural Science Foundation of China(61673186 and 61871196)Beijing Normal University Education Reform Project(jx2024040)Guangdong Undergraduate Universities Teaching Quality and Reform Project(jx2024309).
文摘Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmentation techniques for point clouds,where global augmentation is applied to the entire point cloud of the scene,and cut-paste samples objects from other frames into the current frame.Both types of data augmentation can improve performance,but the cut-paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling,which may be counterproductive and may hurt the overall performance.In addition,LiDAR is susceptible to signal loss,external occlusion,extreme weather and other factors,which can easily cause object shape changes,while global augmentation and cut-paste cannot effectively enhance the robustness of the model.To this end,we propose Syn-Aug,a synchronous data augmentation framework for LiDAR-based 3D object detection.Specifically,we first propose a novel rendering-based object augmentation technique(Ren-Aug)to enrich training data while enhancing scene realism.Second,we propose a local augmentation technique(Local-Aug)to generate local noise by rotating and scaling objects in the scene while avoiding collisions,which can improve generalisation performance.Finally,we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames.We verify the proposed framework with four different types of 3D object detectors.Experimental results show that our proposed Syn-Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets,proving the effectiveness and generality of Syn-Aug.On KITTI,four different types of baseline models using Syn-Aug improved mAP by 0.89%,1.35%,1.61%and 1.14%respectively.On nuScenes,four different types of baseline models using Syn-Aug improved mAP by 14.93%,10.42%,8.47%and 6.81%respectively.The code is available at https://github.com/liuhuaijjin/Syn-Aug.
文摘With the widespread application of 3D visualization in digital exhibition halls and virtual reality,achieving efficient rendering and high-fidelity presentation has become a key challenge.This study proposes a hybrid point cloud generation method that combines traditional sampling with 3D Gaussian splatting,aiming to address the issues of rendering delay and missing details in existing 3D displays.By improving the OBJ model parsing process and incorporating an adaptive area-weighted sampling algorithm,we achieve adaptive point cloud generation based on triangle density.Innovatively,we advance the ellipsoidal parameter estimation process of 3D Gaussian splatting to the point cloud generation stage.By establishing a mathematical relationship between the covariance matrix and local curvature,the generated point cloud naturally exhibits Gaussian distribution characteristics.Experimental results show that,compared to traditional methods,our approach reduces point cloud data by 38% while maintaining equivalent visual quality at a 4096×4096 texture resolution.By introducing mipmap texture optimization strategies and a GPU-accelerated rasterization pipeline,stable rendering at 60 frames per second is achieved in a WebGL environment.Additionally,we quantize and compress the spherical harmonic function parameters specific to 3D Gaussian splatting,reducing network transmission bandwidth to 52% of the original data.This study provides a new technical pathway for fields requiring high-precision display,such as the digitization of cultural heritage.
