Three-dimensional(3D)parametric measurements of individual gravels in the Gobi region using point cloud technique

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.

Building 3D CityGML models of mining industrial structures using integrated UAV and TLS point clouds

Mining industrial areas with anthropogenic engineering structures are one of the most distinctive features of the real world.3D models of the real world have been increasingly popular with numerous applications,such as digital twins and smart factory management.In this study,3D models of mining engineering structures were built based on the CityGML standard.For collecting spatial data,the two most popular geospatial technologies,namely UAV-SfM and TLS were employed.The accuracy of the UAV survey was at the centimeter level,and it satisfied the absolute positional accuracy requirement of creat-ing all levels of detail(LoD)according to the CityGML standard.Therefore,the UAV-SfM point cloud dataset was used to build LoD 2 models.In addition,the comparison between the UAV-SfM and TLS sub-clouds of facades and roofs indicates that the UAV-SfM and TLS point clouds of these objects are highly consistent,therefore,point clouds with a higher level of detail and accuracy provided by the integration of UAV-SfM and TLS were used to build LoD 3 models.The resulting 3D CityGML models include 39 buildings at LoD 2,and two mine shafts with hoistrooms,headframes,and sheave wheels at LoD3.

Indoor Space Modeling and Parametric Component Construction Based on 3D Laser Point Cloud Data

In order to enhance modeling efficiency and accuracy,we utilized 3D laser point cloud data for indoor space modeling.Point cloud data was obtained with a 3D laser scanner and optimized with Autodesk Recap and Revit software to extract geometric information about the indoor environment.Furthermore,we proposed a method for constructing indoor elements based on parametric components.The research outcomes of this paper will offer new methods and tools for indoor space modeling and design.The approach of indoor space modeling based on 3D laser point cloud data and parametric component construction can enhance modeling efficiency and accuracy,providing architects,interior designers,and decorators with a better working platform and design reference.

Advancing Wound Filling Extraction on 3D Faces:An Auto-Segmentation and Wound Face Regeneration Approach

Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications.In this paper,we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network.Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions.To achieve accurate segmentation,we conducted thorough experiments and selected a high-performing model from the trainedmodels.The selectedmodel demonstrates exceptional segmentation performance for complex 3D facial wounds.Furthermore,based on the segmentation model,we propose an improved approach for extracting 3D facial wound fillers and compare it to the results of the previous study.Our method achieved a remarkable accuracy of 0.9999993% on the test suite,surpassing the performance of the previous method.From this result,we use 3D printing technology to illustrate the shape of the wound filling.The outcomes of this study have significant implications for physicians involved in preoperative planning and intervention design.By automating facial wound segmentation and improving the accuracy ofwound-filling extraction,our approach can assist in carefully assessing and optimizing interventions,leading to enhanced patient outcomes.Additionally,it contributes to advancing facial reconstruction techniques by utilizing machine learning and 3D bioprinting for printing skin tissue implants.Our source code is available at https://github.com/SIMOGroup/WoundFilling3D.

Aggregate Point Cloud Geometric Features for Processing

As 3D acquisition technology develops and 3D sensors become increasingly affordable,large quantities of 3D point cloud data are emerging.How to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be solved.The point cloud geometric information is hidden in disordered,unstructured points,making point cloud analysis a very challenging problem.To address this problem,we propose a novel network framework,called Tree Graph Network(TGNet),which can sample,group,and aggregate local geometric features.Specifically,we construct a Tree Graph by explicit rules,which consists of curves extending in all directions in point cloud feature space,and then aggregate the features of the graph through a cross-attention mechanism.In this way,we incorporate more point cloud geometric structure information into the representation of local geometric features,which makes our network perform better.Our model performs well on several basic point clouds processing tasks such as classification,segmentation,and normal estimation,demonstrating the effectiveness and superiority of our network.Furthermore,we provide ablation experiments and visualizations to better understand our network.

Fast Estimation of Loader’s Shovel Load Volume by 3D Reconstruction of Material Piles

Fast and accurate measurement of the volume of earthmoving materials is of great signifcance for the real-time evaluation of loader operation efciency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader’s shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fll factor estimation, and it has signifcant theoretical research and engineering application value.

3D Object Detection with Attention:Shell-Based Modeling

LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box positioning.To address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate information.To improve the detection accuracy,a shell-based modeling method is proposed.It roughly determines which spherical shell the coordinates belong to.Then,the results are refined to ground truth,thereby narrowing the localization range and improving the detection accuracy.To improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection structure.Some of these features are highlighted by weighting them on the feature dimensions.After training,it makes the feature weights that are favorable for object detection get larger.Thus,the extracted features are more adapted to the object detection task.Extensive comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.

Visual perception driven 3D building structure representa tion from airborne laser scanning point cloud

Background Three-dimensional(3D)building models with unambiguous roof plane geometry parameters,roof structure units,and linked topology provide essential data for many applications related to human activities in urban environments.The task of 3D reconstruction from point clouds is still in the development phase,especially the recognition and interpretation of roof topological structures.Methods This study proposes a novel visual perception-based approach to automatically decompose and reconstruct building point clouds into meaningful and simple parametric structures,while the associated mutual relationships between the roof plane geometry and roof structure units are expressed by a hierarchical topology tree.First,a roof plane extraction is performed by a multi-label graph cut energy optimization framework and a roof structure graph(RSG)model is then constructed to describe the roof topological geometry with common adjacency,symmetry,and convexity rules.Moreover,a progressive roof decomposition and refinement are performed,generating a hierarchical representation of the 3D roof structure models.Finally,a visual plane fitted residual or area constraint process is adopted to generate the RSG model with different levels of details.Results Two airborne laser scanning datasets with different point densities and roof styles were tested,and the performance evaluation metrics were obtained by International Society for Photogrammetry and Remote Sensing,achieving a correctness and accuracy of 97.7%and 0.29m,respectively.Conclusions The standardized assessment results demonstrate the effectiveness and robustness of the proposed approach,showing its ability to generate a variety of structural models,even with missing data.

Research on BIM Model Reshaping Method Based on 3D Point Cloud Recognition

In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technology and BIM(Building Information Modeling)model was discussed.Focused on the efficient acquisition of building geometric information using the fast-developing 3D point cloud technology,an improved deep learning-based 3D point cloud recognition method was proposed.The method optimised the network structure based on RandLA-Net to adapt to the large-scale point cloud processing requirements,while the semantic and instance features of the point cloud were integrated to significantly improve the recognition accuracy and provide a precise basis for BIM model remodeling.In addition,a visual BIM model generation system was developed,which systematically transformed the point cloud recognition results into BIM component parameters,automatically constructed BIM models,and promoted the open sharing and secondary development of models.The research results not only effectively promote the automation process of converting 3D point cloud data to refined BIM models,but also provide important technical support for promoting building informatisation and accelerating the construction of smart cities,showing a wide range of application potential and practical value.

Indoor 3D Reconstruction Using Camera, IMU and Ultrasonic Sensors

The recent advances in sensing and display technologies have been transforming our living environments drastically. In this paper, a new technique is introduced to accurately reconstruct indoor environments in three-dimensions using a mobile platform. The system incorporates 4 ultrasonic sensors scanner system, an HD web camera as well as an inertial measurement unit (IMU). The whole platform is mountable on mobile facilities, such as a wheelchair. The proposed mapping approach took advantage of the precision of the 3D point clouds produced by the ultrasonic sensors system despite their scarcity to help build a more definite 3D scene. Using a robust iterative algorithm, it combined the structure from motion generated 3D point clouds with the ultrasonic sensors and IMU generated 3D point clouds to derive a much more precise point cloud using the depth measurements from the ultrasonic sensors. Because of their ability to recognize features of objects in the targeted scene, the ultrasonic generated point clouds performed feature extraction on the consecutive point cloud to ensure a perfect alignment. The range measured by ultrasonic sensors contributed to the depth correction of the generated 3D images (the 3D scenes). Experiments revealed that the system generated not only dense but precise 3D maps of the environments. The results showed that the designed 3D modeling platform is able to help in assistive living environment for self-navigation, obstacle alert, and other driving assisting tasks.

Visualization Analysis for 3D Big Data Modeling

This paper describes an automatic system for 3D big data of face modeling using front and side view images taken by an ordinary digital camera, whose directions are orthogonal. The paper consists of four keys in 3D visualization. Firstly we study the 3D big data of face modeling including feature facial extraction from 2D images. The second part is to represent the technical from Computer Vision, Image Processing and my new method for extract information from images and create 3D model. Thirdly, 3D face modeling based on 2D image software is implemented by C# language, EMGU CV library and XNA framework. Finally, we design experiment, test and record results for measure performance of our method.

An Automated Process of Creating 3D City Model for Monitoring Urban Infrastructures

This paper describes the process of designing models and tools for an automated way of creating 3D city model based on a raw point cloud.Also,making and forming 3D models of buildings.Models and tools for creating tools made in the model builder application within the ArcGIS Pro software.An unclassified point cloud obtained by the LiDAR system was used for the model input data.The point cloud,collected by the airborne laser scanning system(ALS),is classified into several classes:ground,high and low noise,and buildings.Based on the created DEMs,points classified as buildings and formed prints of buildings,realistic 3D city models were created.Created 3D models of cities can be used as a basis for monitoring the infrastructure of settlements and other analyzes that are important for further development and architecture of cities.

Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training

3D morphable models(3DMMs)are generative models for face shape and appearance.Recent works impose face recognition constraints on 3DMM shape parameters so that the face shapes of the same person remain consistent.However,the shape parameters of traditional 3DMMs satisfy the multivariate Gaussian distribution.In contrast,the identity embeddings meet the hypersphere distribution,and this conflict makes it challenging for face reconstruction models to preserve the faithfulness and the shape consistency simultaneously.In other words,recognition loss and reconstruction loss can not decrease jointly due to their conflict distribution.To address this issue,we propose the Sphere Face Model(SFM),a novel 3DMM for monocular face reconstruction,preserving both shape fidelity and identity consistency.The core of our SFM is the basis matrix which can be used to reconstruct 3D face shapes,and the basic matrix is learned by adopting a twostage training approach where 3D and 2D training data are used in the first and second stages,respectively.We design a novel loss to resolve the distribution mismatch,enforcing that the shape parameters have the hyperspherical distribution.Our model accepts 2D and 3D data for constructing the sphere face models.Extensive experiments show that SFM has high representation ability and clustering performance in its shape parameter space.Moreover,it produces highfidelity face shapes consistently in challenging conditions in monocular face reconstruction.The code will be released at https://github.com/a686432/SIR.

基于特征点动态选择的三维人脸点云模型重建

针对典型的点云配准方法中伪特征点过多导致配准效率低和配准结果不精确的问题,提出一种基于特征点动态选择的三维人脸点云模型重建方法。该方法在粗配准阶段,采用动态特征矩阵求解法获取粗匹配特征变换矩阵以避免伪特征点的干扰。在精配准过程中,采用二次加权法向量垂直距离法在人脸流形表面选择更有效的特征点以减少伪特征点的数量,并采用基于特征融合与局部特征一致性的迭代最近点方法进行精配准。经过对比实验验证了算法的可行性,实验结果表明,该算法能够实现高精度且快速的三维人脸点云模型重建,且均方根误差达到1.8165 mm,相较其他算法,其在模型重建精度和效率方面都有所提升,具有良好的应用前景。

基于MATLAB环境下的沥青道面表观纹理重构和分形特征研究

鉴于道面微观构造对于道面抗滑性能的重要性,文中选取变化关键筛孔的8条曲线,进行试样成型研究。借助纹理激光仪AMES进行表观纹理提取,运用MATLAB软件三维重构技术,并使用分形理论的盒子计数法,研究纹理参数与抗滑性能指标的相关性。研究表明,MATLAB软件重构三维模型高精度还原沥青道面的表观形貌,道面构造参数与抗滑性能之间具有良好的线性关系,可为后续预测道面长期抗滑性提供保障。

Neighborhood co-occurrence modeling in 3D point cloud segmentation

A significant performance boost has been achieved in point cloud semantic segmentation by utilization of the encoder-decoder architecture and novel convolution operations for point clouds.However,co-occurrence relationships within a local region which can directly influence segmentation results are usually ignored by current works.In this paper,we propose a neighborhood co-occurrence matrix(NCM)to model local co-occurrence relationships in a point cloud.We generate target NCM and prediction NCM from semantic labels and a prediction map respectively.Then,Kullback-Leibler(KL)divergence is used to maximize the similarity between the target and prediction NCMs to learn the co-occurrence relationship.Moreover,for large scenes where the NCMs for a sampled point cloud and the whole scene differ greatly,we introduce a reverse form of KL divergence which can better handle the difference to supervise the prediction NCMs.We integrate our method into an existing backbone and conduct comprehensive experiments on three datasets:Semantic3D for outdoor space segmentation,and S3DIS and ScanNet v2 for indoor scene segmentation.Results indicate that our method can significantly improve upon the backbone and outperform many leading competitors.

The Integrated 3D As-Built Representation of Underground MRT Construction Sites

This study facilitates the scalability of as-built data from an earlier street level to underground transportation sites from the life-cycle perspective of urban information maintenance. As-built 3D scans of a 6 km street were made at different time periods, and of 3 underground Mass Rapid Transit (MRT) stations under construction in Taipei. A scanned point cloud was used to create a Building Information Modeling (BIM) Level of Development (LOD) 500 as-built point cloud model, with which topographic utility data were integrated and the model quality was investigated. The complex underground models of the transportation stations are proofed to be in correct relative locations to the street entrances on ground level. In the future the 3D relationship around the station will facilitate new designs or excavations in the neighborhood urban environment.

全局ICP与改进泊松相结合的三维人脸重建

为快速精确地实现人脸三维数字化,本文提出一种高精度全流程自动化的稳健三维人脸重建方法。针对基于结构光相机采集到的左右两组人脸点云和RGB图像数据,本文首先提出一种自适应下采样的全局优化ICP配准方法融合左右点云,其次提出基于法向量优化的泊松重建方法来将配准后的点云进行表面重建,生成网格化模型,该泊松重建方法针对非封闭性人脸点云有良好的重建效果和重建精度,然后将RGB图像贴图到网格化模型上,最终重建出了一个带有细节纹理的三维人脸模型。经过大量的人脸重建试验验证,本文方法具有高精度、高稳健性,能够快速、准确且稳定地重建出人脸。

A novel method for extracting skeleton of fruit treefrom 3D point clouds

Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree.The phenomenon of organs’mutual occlusion in fruit tree canopy is usually very serious,this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree.However,traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points.To overcome this limitation,we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds.The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction,then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction.The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.

基于自旋图的三维自动目标识别

将三维表面匹配领域的自旋图(spin-images)方法运用于扫描激光雷达自动目标识别中,基于自旋图特征,提出了平均最大相似性度量、候选对应关系数目、几何一致性筛选后对应关系数目、分组筛选后最大组的对应关系数目这4个识别指标,并将其组合构成组合识别准则,从而进行三维目标识别。同时针对实际应用需求,进一步研究了点云空间分辨率、激光雷达测距误差以及遮挡对目标识别率的影响,为激光雷达三维目标识别系统的设计提供了参考。