Improvement of Binocular Reconstruction Algorithm for Measuring 3D Pavement Texture Using a Single Laser Line Scanning Constraint

The dense and accurate measurement of 3D texture is helpful in evaluating the pavement function.To form dense mandatory constraints and improve matching accuracy,the traditional binocular reconstruction technology was improved threefold.First,a single moving laser line was introduced to carry out global scanning constraints on the target,which would well overcome the difficulty of installing and recognizing excessive laser lines.Second,four kinds of improved algorithms,namely,disparity replacement,superposition synthesis,subregion segmentation,and subregion segmentation centroid enhancement,were established based on different constraint mechanism.Last,the improved binocular reconstruction test device was developed to realize the dual functions of 3D texture measurement and precision self-evaluation.Results show that compared with traditional algorithms,the introduction of a single laser line scanning constraint is helpful in improving the measurement’s accuracy.Among various improved algorithms,the improvement effect of the subregion segmentation centroid enhancement method is the best.It has a good effect on both overall measurement and single pointmeasurement,which can be considered to be used in pavement function evaluation.

Using MC Algorithm to Implement 3D Image Reconstruction for Yunnan Weather Radar Data

3D image reconstruction for weather radar data can not only help the weatherman to improve the forecast efficiency and accuracy, but also help people to understand the weather conditions easily and quickly. Marching Cubes (MC) algorithm in the surface rendering has more excellent applicability in 3D reconstruction for the slice images;it may shorten the time to find and calculate the isosurface from raw volume data, reflect the shape structure more accurately. In this paper, we discuss a method to reconstruct the 3D weather cloud image by using the proposed Cube Weighting Interpolation (CWI) and MC algorithm. Firstly, we detail the steps of CWI, apply it to project the raw radar data into the cubes and obtain the equally spaced cloud slice images, then employ MC algorithm to draw the isosurface. Some experiments show that our method has a good effect and simple operation, which may provide an intuitive and effective reference for realizing the 3D surface reconstruction and meteorological image stereo visualization.

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.

Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoT-Enhanced Smart Cities

With increasingly more smart cameras deployed in infrastructure and commercial buildings,3D reconstruction can quickly obtain cities’information and improve the efficiency of government services.Images collected in outdoor hazy environments are prone to color distortion and low contrast;thus,the desired visual effect cannot be achieved and the difficulty of target detection is increased.Artificial intelligence(AI)solutions provide great help for dehazy images,which can automatically identify patterns or monitor the environment.Therefore,we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning.First,we propose a fine transmission image deep convolutional regression network(FT-DCRN)dehazing algorithm that uses fine transmission image and atmospheric light value to compute dehazed image.The DCRN is used to obtain the coarse transmission image,which can not only expand the receptive field of the network but also retain the features to maintain the nonlinearity of the overall network.The fine transmission image is obtained by refining the coarse transmission image using a guided filter.The atmospheric light value is estimated according to the position and brightness of the pixels in the original hazy image.Second,we use the dehazed images generated by the FT-DCRN dehazing algorithm for 3D reconstruction.An advanced relaxed iterative fine matching based on the structure from motion(ARI-SFM)algorithm is proposed.The ARISFM algorithm,which obtains the fine matching corner pairs and reduces the number of iterations,establishes an accurate one-to-one matching corner relationship.The experimental results show that our FT-DCRN dehazing algorithm improves the accuracy compared to other representative algorithms.In addition,the ARI-SFM algorithm guarantees the precision and improves the efficiency.

3D reconstruction method based on contour features

To guarantee the accuracy and real-time of the 3D reconstruction method for outdoor scene,an algorithm based on region segmentation and matching was proposed.Firstly,on the basis of morphological gradient information,obtained by comparing color weight gradient images and proposing a multi-threshold segmentation,scene contour features were extracted by a watershed algorithm and a fuzzy c-means clustering algorithm.Secondly,to reduce the search area,increase the correct matching ratio and accelerate the matching speed,the region constraint was established according to a region＇s local position,area and gray characteristics,the edge pixel constraint was established according to the epipolar constraint and the continuity constraint.Finally,by using the stereo matching edge pixel pairs,their 3D coordinates were estimated according to the binocular stereo vision imaging model.Experimental results show that the proposed method can yield a high stereo matching ratio and reconstruct a 3D scene quickly and efficiently.

PARALLEL IMPLEMENTATION AND OPTIMIZATION OF THE SEBVHOS ALGORITHM

In this paper, a parallel Surface Extraction from Binary Volumes with Higher-Order Smoothness (SEBVHOS) algorithm is proposed to accelerate the SEBVHOS execution. The original SEBVHOS algorithm is parallelized first, and then several performance optimization techniques which are loop optimization, cache optimization, false sharing optimization, synchronization overhead op-timization, and thread affinity optimization, are used to improve the implementation's performance on multi-core systems. The performance of the parallel SEBVHOS algorithm is analyzed on a dual-core system. The experimental results show that the parallel SEBVHOS algorithm achieves an average of 1.86x speedup. More importantly, our method does not come with additional aliasing artifacts, com-paring to the original SEBVHOS algorithm.

New Algorithm for 3D Facial Model Reconstruction and Its Application in Virtual Reality

3D human face model reconstruction is essential to the generation of facial animations that is widely used in the field of virtual reality (VR). The main issues of 3D facial model reconstruction based on images by vision technologies are in twofold: one is to select and match the corresponding features of face from two images with minimal interaction and the other is to generate the realistic-looking human face model. In this paper, a new algorithm for realistic-looking face reconstruction is presented based on stereo vision. Firstly, a pattern is printed and attached to a planar surface for camera calibration, and corners generation and corners matching between two images are performed by integrating modified image pyramid Lucas-Kanade (PLK) algorithm and local adjustment algorithm, and then 3D coordinates of corners are obtained by 3D reconstruction. Individual face model is generated by the deformation of general 3D model and interpolation of the features. Finally, realistic-looking human face model is obtained after texture mapping and eyes modeling. In addition, some application examples in the field of VR are given. Experimental result shows that the proposed algorithm is robust and the 3D model is photo-realistic.

DLT算法中象平面误差对三维重构的影响

DLT算法是利用平面图象进行三维重构的广泛采用的基本算法。本文针对平面图象处理中难以避免的象坐标误差 ,讨论了各坐标误差对三维重构坐标的影响 ,由此分析了三维重构坐标对各象坐标误差的敏感程度 ,并进行了数值模拟实验。结果表明三维重构的各分坐标对象坐标误差具有不同的敏感度 ,而且与相机的空间位置有密切的联系。这说明DLT算法中虽然在理论上对相机的空间配置无特别要求 。

DLT算法中框架位置对三维重构精度的影响

直接线性变换(DLT)算法是利用平面图像进行三维重构的基本算法,本文从框架位置的角度对三维重构的影响进行讨论,由此得出各个框架位置下控制点的平均相对误差,结果表明框架位置对三维重构具有一定的影响,同时相机角度对三雏重构具有深远的意义。

3D Face Reconstruction Using Images from Cameras with Varying Parameters

In this paper, we present a new technique of 3D face reconstruction from a sequence of images taken with cameras having varying parameters without the need to grid. This method is based on the estimation of the projection matrices of the cameras from a symmetry property which characterizes the face, these projections matrices are used with points matching in each pair of images to determine the 3D points cloud, subsequently, 3D mesh of the face is constructed with 3D Crust algorithm. Lastly, the 2D image is projected on the 3D model to generate the texture mapping. The strong point of the proposed approach is to minimize the constraints of the calibration system： we calibrated the cameras from a symmetry property which characterizes the face, this property gives us the opportunity to know some points of 3D face in a specific well-chosen global reference, to formulate a system of linear and nonlinear equations according to these 3D points, their projection in the image plan and the elements of the projections matrix. Then to solve these equations, we use a genetic algorithm which consists of finding the global optimum without the need of the initial estimation and allows to avoid the local minima of the formulated cost function. Our study is conducted on real data to demonstrate the validity and the performance of the proposed approach in terms of robustness, simplicity, stability and convergence.

曲面散乱点集的组合三角剖分法

曲面散乱点集的三角剖分广泛应用于三维重建领域.为了更加快速、准确的完成曲面重建,提出了一种组合三角剖分法.此方法将整个剖分过程分为三个步骤:首先借鉴分治算法的思想将整个点集进行区域划分,以降低其拓扑结构的复杂性;之后在各个小区域内依据异侧准则、法向量夹角最大准则、域值距离准则和最小内角最大准则进行直接三角剖分;最后根据三维Delaunay空球准则进行各区域边界的连接,从而完成剖分.实验结果表明,组合法可以准确、快速的实现曲面散乱点集的三角剖分.

RANSAC平面估计算法在路面物体体积测量中的应用

在利用车载单线式激光扫描仪实施路面三维重建及路面目标物体积测量的应用中,使用RANSAC平面估计算法获得了路面的0高度平面,提高了路面目标物体积计算的精度。实际测试表明,对路面上一不规则形状坑,采用注水法测量得到的坑体积为2.25 m3,点云经优化前后计算得到的坑体积分别为1.86 m3和2.10 m3,误差分别为17.3%和6.7%,点云优化过程耗时约7 s。因此,本文的路面三维点云优化方法速度快,且三维重建效果及目标物的体积计算精度可满足实际要求。

基于三投影方向的层析重建分析

针对传统的层析方法中,投影方向多、系统复杂等特点,提出了基于三个投影方向的层析重建方法.论文选取适用于少投影方向的代数迭代重建算法,采用参量修正方法,能较好的提高重建准确度和抑制椒盐噪音.同时对影响重建图像质量和速度的迭代次数、不同原图像结构对重建图像质量的影响进行了分析,给出了相应分析模拟结果.结果表明,迭代300次后,重建结果基本稳定.同时对于具有轴对称结构物体,三个方向投影数据能够较好地实现三维层析重建.分析结果为后期开展的基于单幅层析全息图的三维层析重建实验研究提供了理论依据.

基于改进MC算法的三维表面重建

MC算法是面绘制中构造等值面的方法中最具代表性的方法之一,已经得到了许多完善及改进。介绍了改进MC算法的实现步骤,并介绍了用Delphi7.0为开发平台,利用OpenGL三维图形软件包开发基于改进MC算法的三维表面重建系统的设计过程。

用数字全息层析成像技术测量毛细管的内径及壁厚

研究了用数字全息层析成像技术测量微毛细管结构的可行性。考虑毛细管具有理想的柱对称结构,因此采用单幅全息图获取到的物光波复振幅数据来模拟不同角度下的投影数据。分别运用滤波反投影重建算法和傅里叶衍射重建算法对微毛细管进行折射率三维重构;根据重构的折射率切片图,进一步运用相关的边缘提取算法处理得到毛细管的内径及壁厚尺寸。实验结果表明,在合理的光路环境设置下,满足Rytov近似条件下的傅里叶衍射重建算法比滤波反投影重建算法更能够正确反映物体的结构尺寸,更适合用于微小弱散射物体的几何参数测量。实验结果验证了用数字全息方法实现衍射层析重建的可行性,从而为具有柱对称结构的弱散射物体的无损测量提供了一种新的途径。

基于衍射光学元件色散特征的三维建模系统

为了重建复杂形状的三维物体 ,在此提出了一种便捷的基于衍射光学元件 (DOE)色散成像特性的三维建模算法 ,该算法在一种新型的三维建模系统中实现 ,它充分利用了DOE在红外波段的模式投影和探测 给出了重建复杂形状的三维物体的仿真试验结果 。

油气管道漏磁检测缺陷的三维成像技术

漏磁检测是油气管道常用的无损检测方法,检测的重点是根据测量的漏磁信号重构缺陷的轮廓。提出了基于小波神经网络的三维成像方法,利用图像函数矩阵表达出管道缺陷的三维图像,矩阵元素值对应着缺陷的深度。利用小波神经网络,建立了由缺陷漏磁信号到图像函数矩阵关系的映射。选用的小波函数是墨西哥草帽小波,采用随机梯度下降算法训练。训练样本为三维有限元仿真数据和测量数据。采用训练数据对小波神经网络进行逼近缺陷图像函数矩阵的训练,然后用训练好的小波神经网反演给定数据,重构缺陷图像。实验结果表明,该方法能够实现三维缺陷漏磁检测的成像化及可视化。

面向三维点云测量的双目立体匹配算法

在随机光场的辅助照射下,综合利用图像信息和几何约束信息,对匹配施加更多的约束,在匹配中即可以得到三维重建结果,从而使立体图像匹配和三维点云生成过程融为一体;提出了一种带权值的匹配窗口,在一定程度上改善了匹配效果;基于连续性约束,提出了用生长法给出初始匹配,可以极大地减少算法迭代次数,提高匹配速度。为了能够进一步提高算法的实用性,还讨论了灰度矫正处理方式对算法的影响。在实验中,完成了实际物体的三维表面点云重建,并与典型商用系统进行了重建结果的对比分析,验证了本文算法在精度和三维重建效果方面的优越性。

移动立方体算法中的三角剖分

MarchingCubes(MC)算法是基于规则体数据抽取等值面的经典算法。分析了该算法中的交点连接问题,解决连接上的二义性问题,从而更好地生成多边形;对于生成的非平面多边形,对三角剖分进行了优化,以此改进了移动立方体算法,通过实验验证了算法的正确性。

基于改进测量系统的非定步距相位轮廓测量技术研究

提出一种基于改进测量系统,该系统放宽了传统测量光路的3个约束,降低对系统结构的要求;同时结合非定步距轮廓测量技术,与传统相位轮廓测量技术相比较,无需保证等步距相移量,且仅需两幅畸变光栅图像就可以重建物体的三维轮廓。通过理论分析,推导出高度与相位之间的非线性映射函数关系式。实验证明,在满足一定精度要求的同时,该系统对结构条件要求较低,测量速度较快,具有较强的可操作性。