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.

A modified method of discontinuity trace mapping using three-dimensional point clouds of rock mass surfaces

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.

Three-dimensional face point cloud hole-filling algorithm based on binocular stereo matching and a B-spline

When obtaining three-dimensional(3D)face point cloud data based on structured light,factors related to the environment,occlusion,and illumination intensity lead to holes in the collected data,which affect subsequent recognition.In this study,we propose a hole-filling method based on stereo-matching technology combined with a B-spline.The algorithm uses phase information acquired during raster projection to locate holes in the point cloud,simultaneously extracting boundary point cloud sets.By registering the face point cloud data using the stereo-matching algorithm and the data collected using the raster projection method,some supplementary information points can be obtained at the holes.The shape of the B-spline curve can then be roughly described by a few key points,and the control points are put into the hole area as key points for iterative calculation of surface reconstruction.Simulations using smooth ceramic cups and human face models showed that our model can accurately reproduce details and accurately restore complex shapes on the test surfaces.Simulation results indicated the robustness of the method,which is able to fill holes on complex areas such as the inner side of the nose without a prior model.This approach also effectively supplements the hole information,and the patched point cloud is closer to the original data.This method could be used across a wide range of applications requiring accurate facial recognition.

A state-of-the-art review of automated extraction of rock mass discontinuity characteristics using three-dimensional surface models

In the last two decades,significant research has been conducted in the field of automated extraction of rock mass discontinuity characteristics from three-dimensional(3D)models.This provides several methodologies for acquiring discontinuity measurements from 3D models,such as point clouds generated using laser scanning or photogrammetry.However,even with numerous automated and semiautomated methods presented in the literature,there is not one single method that can automatically characterize discontinuities accurately in a minimum of time.In this paper,we critically review all the existing methods proposed in the literature for the extraction of discontinuity characteristics such as joint sets and orientations,persistence,joint spacing,roughness and block size using point clouds,digital elevation maps,or meshes.As a result of this review,we identify the strengths and drawbacks of each method used for extracting those characteristics.We found that the approaches based on voxels and region growing are superior in extracting joint planes from 3D point clouds.Normal tensor voting with trace growth algorithm is a robust method for measuring joint trace length from 3D meshes.Spacing is estimated by calculating the perpendicular distance between joint planes.Several independent roughness indices are presented to quantify roughness from 3D surface models,but there is a need to incorporate these indices into automated methodologies.There is a lack of efficient algorithms for direct computation of block size from 3D rock mass surface models.

Integration system research and development for three-dimensional laser scanning information visualization in goaf

An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.

Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud

The human face is a valuable biomarker of aging,but the collection and use of its image raise significant privacy concerns.Here we present an approach for facial data masking that preserves age-related features using coordinate-wise monotonic transformations.We first develop a deep learning model that estimates age directly from non-registered face point clouds with high accuracy and generalizability.We show that the model learns a highly indistinguishable mapping using faces treated with coordinate-wise monotonic transformations,indicating that the relative positioning of facial information is a low-level biomarker of facial aging.Through visual perception tests and computational3D face verification experiments,we demonstrate that transformed faces are significantly more difficult to perceive for human but not for machines,except when only the face shape information is accessible.Our study leads to a facial data protection guideline that has the potential to broaden public access to face datasets with minimized privacy risks.

基于残差优化的综采工作面煤壁点云补全方法

煤矿综采工作面巷道的数字化三维重建过程中需要完整且密集的煤壁点云数据。受遮挡、视角限制等因素影响,采集的综采工作面煤壁点云数据往往不完整且稀疏,影响下游任务,需进行煤壁点云修复和补全。目前缺少针对井下点云补全任务的数据集和网络模型,现有模型用于煤壁点云补全时存在点云密度分布不均匀、点云特征信息丢失等情况。针对上述问题,设计了一种基于残差优化的煤壁点云补全网络模型,采用监督学习方式学习点云特征信息,通过最小化密度采样和残差网络迭代优化输出完整点云。采集煤矿井下真实综采工作面煤壁点云数据,预处理后筛选可用数据,通过模拟随机空洞制作煤壁点云缺失数据集,并用缺失数据集训练基于残差优化的煤壁点云补全网络模型。实验结果表明:与经典的FoldingNet,TopNet,AtlasNet,PCN,3D-Capsule点云补全网络模型相比,基于残差优化的煤壁点云补全网络模型针对构造的缺失煤壁点云和稀疏煤壁点云补全的倒角距离、地移距离及F1分数均能达到最优水平,整体补全效果最佳;针对实际缺失的煤壁点云,该模型能够实现有效补全。

基于多尺度时空特征和篡改概率改善换脸检测的跨库性能

目前大多DeepFake换脸检测算法过于依赖局部特征,尽管库内检测性能尚佳,但容易出现过拟合,导致跨库检测性能不理想,即泛化性能不够好。有鉴于此,文中提出一种基于多尺度时空特征和篡改概率的换脸视频检测算法,目的是利用假脸视频中广泛存在的帧间时域不连续性缺陷来解决现有检测算法在跨库、跨伪造方式和视频压缩时性能明显下降的问题,改善泛化检测能力。该算法包括3个模块:为检测假脸视频在时域上留下的不连续痕迹,设计了一个多尺度时空特征提取模块;为自适应计算多尺度时空特征之间的时空域关联性,设计了一个三维双注意力机制模块;为预测随机选取的像素点的篡改概率和构造监督掩膜,设计了一个辅助监督模块。将所提出的算法在FF++、DFD、DFDC、CDF等公开大型标准数据库中进行实验,并与基线算法和近期发布的同类算法进行对比。结果显示:文中算法在保持库内平均检测性能优良的同时,跨库检测和抗视频压缩时的综合性能最好,跨伪造方法检测时的综合性能中等偏上。实验结果验证了文中算法的有效性。

基于关键特征增强机制的3D人脸识别

3D人脸识别是计算机视觉领域的重要组成部分,Pointnet依靠深度学习解决了点云的无序性,实现了3D点云的全局特征提取,但由于点云数据缺乏细节纹理,仅靠全局特征很难实现复杂情况下的人脸识别。针对以上问题,基于Pointnet提出了一种局部特征描述子,用于描述点云局部空间的几何特征,并引入关键特征增强机制,通过特征概率分布增强人脸关键信息,该机制能减少不必要特征对任务的干扰,有效提升模型的准确率。在公共数据集CASIA-3D、Lock3DFace、Bosphorus上进行实验测试,结果表明该方法能很好地应对表情变化、部分遮挡以及头部姿态的干扰,在弱光环境下其准确率高于RP-Net 1.1%,并具有良好的实时性。

采用点云分区统计的成捆棒材端面定位方法

针对成捆棒材端面贴标过程中出现的端面中心位姿测量难度大、效率低等问题,提出一种基于点云分区统计的成捆棒材端面定位方法.首先对端面点云进行预处理操作;然后使用改进的聚类分割算法对端面点云进行分割,并以分区统计为依据提出完整单根棒材端面中心定位方法和残缺单根棒材端面中心定位方法,实现端面中心定位;最后以端面法向量表征端面中心姿态重建端面中心位姿.采集200组不同姿态的单根棒材端面点云进行实验的结果表明,单根棒材端面定位时间不超过0.5 s,位置误差小于3 mm,姿态误差小于2°;所提方法是高效的,可准确地定位棒材端面中心.

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。

综采工作面顶板与煤壁交线提取算法研究

顶板与煤壁交线信息作为指导采煤机滚筒调高的重要依据,其提取速度与精度直接影响到采煤机自动截煤的效果。对综采工作面的顶板与煤壁交线提取算法进行了研究。通过将综采工作面的点云模型沿着工作面机头机尾方向进行分割,将整个工作面的点云模型分割成数个薄片;对每个小块进行数据处理,提取一个点作为当前位置的顶板线上的点;将每个点云段的顶板线线提取后,对这些点使用样条曲线进行数据拟合,这条拟合出的曲线可作为顶板与煤壁交线。通过该方法不仅可以快速提取顶板线,而且顶板线的平均误差小于9 cm,可以很好地指导采煤机滚筒自动调高。

基于点云和RGB的三维增强人脸识别

针对普通相机无法获取人脸图片的深度信息而导致识别率低的问题,提出了一种基于点云和RGB图像的三维人脸增强识别方法。首先,利用深度相机采集点云数据和RGB图像,并利用Transformer的Attention机制建立图像的融合机制。其次,在FaceNet的MobileNet中增加encoder网络层,即PointTransformer,可以将数据分成多块计算。在encoder网络中,利用Attention机制将点云与RGB图像融合,以增强3D人脸识别的准确率。结果表明:基于点云和RGB图像的三维人脸增强识别方法的准确率达到99.67%,验证了此方法的可行性。

Complex geometric modeling and tooth contact analysis of a helical face gear pair with arc-tooth

A complex geometric modeling method of a helical face gear pair with arc-tooth generated by an arc-profile cutting(APC)disc is proposed,and its tooth contact characteristics are analyzed.Firstly,the spatial coordinate system of an APC face gear pair is established based on meshing theory.Combining the coordinate transformation matrix and the tooth profile of the cutter,the equations of the curve envelope of the APC face gear pair are obtained.Then the surface equations are solved to extract the point clouds data by programming in MATLAB,which contains the work surface and the fillet surface of the APC face gear pair.And the complex geometric model of the APC face gear pair is built by fitting its point clouds.At last,through the analysis of the tooth surface contact,the sensitivity of the APC face gear to the different types of mounting errors is obtained.The results show that the APC face gear pair is the most sensitive to mounting errors in the tooth thickness direction,and it should be strictly controlled in the actual application.

三维深度点云监督和置信度修正的人脸欺诈检测算法

基于深度学习的人脸身份认证由于使用便捷和用户体验好,成为我国当今最受欢迎的人工智能技术应用之一.人脸识别和认证系统必须确保所比对的人脸是真实人脸,否则输出的结果没有任何商业价值.位于系统前端的人脸欺诈检测也称活体检测是保障人脸识别和认证系统有效输出的关键.现有人脸欺诈检测算法虽然库内性能尚佳,但由于实验室训练环境无法完全模拟真实应用场景,造成源域和目标域的数据在分布上存在差异,导致跨库检测性能明显下降.尽管通过增加检测特征的种类和个数可以改善算法性能,但会导致检测网络构造复杂,模型变大,计算复杂度增加.为了改善算法的跨库检测性能并降低计算的复杂度,本文提出一种基于三维(3D)深度点云监督和置信度修正机制的人脸欺诈检测算法.主要贡献包括:设计了DenseBlockNet,仅用较浅层的DenseBlockNet网络即可提取真假人脸之间具有很好区分度的深度信息特征,模型小;将DenseBlockNet输出的二维深度图与采样点位置进行关联,构造三维深度点云,采用倒角损失函数监督预测的深度点云与实际点云标签之间的三维空间距离,同时还采用图二元交叉熵损失监督预测的深度图与深度图标签之间的差异;在3D深度点云预测模块中引入置信度修正机制,修正二分类误差,同时避免库内过拟合,提高算法的泛化能力.所提出方法与包括2种最新文献的8种典型算法在Replay-attack、CASIA-FASD、MSU-MFSD、Rose-Youtu、OULU-NPU等5个主流人脸欺诈检测数据库上进行了充分的对比实验,实验结果表明,所提出的算法在库内和跨库检测中均能保持半总错误率最低或次低,且模型最小,参数量最少,计算复杂度最低.

基于点云匹配的AR饰面作业系统跟踪注册方法

针对建筑机器人饰面作业过程中常因视觉遮挡导致作业效率低的问题,使用增强现实解决遮挡并提出一种基于点云匹配的增强现实跟踪注册方法。利用目标模型点云与作业环境点云的匹配进行目标的初始定位;利用改进的相关滤波跟踪算法对目标进行跟踪获取目标位置;基于迭代最近点法对目标位姿进行估计。在跟踪注册过程中加入位姿优化,保证目标位姿估计精度。为了更加准确地跟踪目标位置,提出一种特征融合和尺度自适应的改进相关滤波目标跟踪算法。通过板材安装实验,表明跟踪注册方法精确性、实时性均较好,最小识别误差达到2.88mm,具有良好的虚实融合效果。

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。

隧道掌子面节理点云识别及微服务模块开发

快速识别岩体隧道掌子面不连续面的点云信息并解译其几何与力学参数是实现隧道远程诊断的重要基础。目前基于数码相片的三维点云识别严重依赖商业程序,本文基于计算机视觉开源框架,自主开发了隧道掌子面不连续面点云信息识别的微服务模块。该微服务模块基于Django框架封装,可灵活部署于任一具有微服务架构的隧道安全诊断平台中,根据用户在线输入的不同视角下隧道掌子面岩体相片,可自动识别三维点云并实现三维重构。该微服务模块已部署于同济大学基础设施智慧服务系统(iS3)中,点云识别结果与其他程序进行了对比,结果表明,该微服务模块能满足基本的三维重构功能要求,在点云识别速度方面具有优势,未来在计算精度方面仍存在提升空间。

A non-contact measurement method for rock mass discontinuity orientations by smartphone

Smartphones are usually packed with a large number of features.An increasing number of researchers are paying attention to the technological capabilities of smartphones,which is a new topic and research interest.This paper proposes a method using smartphones and digital photogrammetry to measure the discontinuity orientation of a rock mass.Smartphone photos satisfying a certain overlap rate provide an efficient method for generating point cloud models of rock outcrops based on image matching.Using the target and the generated point cloud model allows for determining actual geographic coordinates and the measurement of discontinuity orientations.The method proposed has been applied to two different study areas.The discontinuity orientations measured by the proposed method are compared with those measured by the manual method in two cases.The results show a good agreement,verifying the reliability and accuracy of the proposed method.The main contribution of this paper is to use knowledge of coordinate rotation to determine the actual geographic location of the model through a square target.The equipment used in this study is simple,and photogrammetric field surveys are easy to carry out.

基于深度学习的三维人脸识别方法研究

阐述一种基于深度学习的三维人脸识别算法。首先选取合适的人脸数据库,通过点云去噪、孔洞填充、人脸裁剪和姿态校正,生成精确的点云数据;然后,将点云数据输入至能够实现特征重用的DenseNet网络中,完成特征提取,进而采用Arcface损失函数实现人脸分类。