Multi-Branch High-Dimensional Guided Transformer-Based 3D Human Posture Estimation

The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.

3D电影的发展以及NVIDIA 3D VISION平台的搭建

本文简要回顾了3D电影的发展史,并讨论了如何搭建NVIDIA 3D Vision平台,以便在家中欣赏3D大片和玩3D游戏。

Modeling of a Linear Scanning 3D Vision Coordinate Measurement System

This paper theoretically analyzes and researches the coordinate frames of a 3D vision scanning system, establishes the mathematic model of a system scanning process, derives the relationship between the general non-orthonormal sensor coordinate system and the machine coordinate system and the coordinate transformation matrix of the extrinsic calibration for the system.

RANGE ACQUISITION IN BINOCULAR 3-D COMPUTER VISION USING EDGE-BASED HIERARCHICAL MATCHING

This paper deals with a binocular 3-D computer vision system based on the hierarchicalmatching of edge features, Frei and Chen operator is used to extract the edge. The averagegradients of an image obtained by two isotropic operators are non-equal quantized andthresholded in an angle, Edge features are extracted after passing a preemphasis transferfunction which can equalize, the noise affection. Binary edge images are decomposed into apyramid structure which is stored and searched using llliffe’s location method. Corre-sponding points are used to determine the range data using triangulation based on an improvedTrivedi’s formula. In calibration the authors set the optical axes of the two cameras parallelto simplify the calculation, A 3 rd order Householder transform is used to solve the compati-ble coupled equations.

3D Reconstruction of Fruit Shape based on Vision and Edge Sections

The fruit industry has been known as one of the largest businesses in Malaysia,where most of the fruits pass through the peeling process well in advance before the final product as juice in a bottle or slices in a can.The current industrial fruit peeling techniques are passive and inefficient by cutting parts of the pulp of the fruit with peels leading to losses.To avoid this issue,a multi-axis CNC fruit peeler can be used to precisely peel the outer layer with the guidance of a 3D virtual model of fruit.In this work,a new cost-effective method of 3D image reconstruction was developed to convert 36 fruit images captured by a normal RGB camera to a 3D model by capturing a single image every 10 degrees of fruit rotation along a fixed axis.The point cloud data extracted with edge detection were passed to Blender 3D software for meshing in different approaches.The vertical link frame meshing method developed in this research proved a qualitative similarity between the output result and the scanned fruit in a processing time of less than 50 seconds.

Wheel center detection based on stereo vision

As the location of the wheel center is the key to accurately measuring the wheelbase, the wheelbase difference and the wheel static radius, a high-precision wheel center detection method based on stereo vision is proposed. First, according to the prior information, the contour of the wheel hub is extracted and fitted as an ellipse curve, and the ellipse fitting equation can be obtained. Then, a new un-tangent constraint is adopted to improve the ellipse matching precision. Finally, the 3D coordinates of the wheel center can be reconstructed by the spatial circle projection algorithm with low time complexity and high measurement accuracy. Simulation experiments verify that compared with the ellipse center reconstruction algorithm and the planar constraint optimization algorithm, the proposed method can acquire the 3D coordinates of the spatial circle more exactly. Furthermore, the measurements of the wheelbase, the wheelbase difference and the wheel static radius for three types of vehicles demonstrate the effectiveness of the proposed method for wheel center detection.

基于双目视角的嵌入式3D指纹识别系统设计

采用指纹信息作为身份识别手段的应用已经很广泛,但传统的指纹信息识别身份时存在容易被仿造的缺点。3D指纹信息不但可以进一步提高指纹识别率而且可以很好地克服该缺点。在此主要介绍利用双目视角技术采集3D指纹信息,提出了一种3D指纹信息采集的解决方案。详细介绍了以MTK平台作为控制平台,DSP芯片做算法处理的硬件设计。该设计的硬件成本低且安全性高。在C/S结构的系统设计中,通过测试该硬件方案可以直接在客户端上采集信息,便携性好。

3D显示技术在教育领域的应用研究

介绍3D显示相关技术原理和分类,分析3D显示技术在教育教学中的应用优势,3D显示教学对提高学生学习兴趣、参与实践和动手能力,转变教师教学方法,提高教学效果有着重要的意义,同时提出3D显示教学在实际应用中存在的障碍。

3-D计算机视觉系统的高精度摄象机定标算法—DLTEAⅡ

本文在分析计算机视觉系统中摄象机几何模型的基础上,修正了光学镜头畸变模型使之与数字成象系统相配合,提出了一个非常适合于3—D计算机视觉系统的通用高精度摄象机定标方法:增强型直接线性变换—误差拟合算法(DLTEAⅡ).实验表明,使用普通CCD摄象机的立体视觉系统经DLTEAⅡ定标后,在300mm×200mm视场和0.8m～0.9m深度范围内其三维定位的绝对精度分别为dx<0.03mm,dy<0.04mm和dz<0.09mm,相对精度为视场大小和深度的1/10000.本文所提出的定标方法在三维计算机视觉、智能机器人、工业自动化生产、近景摄影测量和军事等领域有着广泛的应用前景.

An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots,which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters.First,the speeded-up robust feature(SURF)extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system.Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image,where the edge contour and the height information of crop row are fused to extract the navigation parameters(θ,d)based on the model of a smart agricultural robot.Finally,the five navigation network instruction sets are designed based on the navigation angleθand the lateral distance d,which represent the basic movements for a certain type of smart agricultural robot working in a field.Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations,and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.

Blade Segment with a 3D Lattice of Diamond Grits Fabricated via an Additive Manufacturing Process

Diamond tools with orderly arrangements of diamond grits have drawn considerable attention in the machining field owing to their outstanding advantages of high sharpness and long service life.This diamond super tool,as well as the manufacturing equipment,has been unavailable to Chinese enterprises for a long time due to patents.In this paper,a diamond blade segment with a 3D lattice of diamond grits was additively manufactured using a new type of cold pressing equipment(AME100).The equipment,designed with a rotary working platform and 16 molding stations,can be used to additively manufacture segments with diamond grits arranged in an orderly fashion,layer by layer;under this additive manufacturing process,at least 216000 pcs of diamond green segments with five orderly arranged grit layers can be produced per month.The microstructure of the segment was observed via SEM and the diamond blade fabricated using these segments was compared to other commercial cutting tools.The experimental results showed that the 3D lattice of diamond grits was formed in the green segment.The filling rate of diamond grits in the lattice could be guaranteed to be above 95%;this is much higher than the 90%filling rate of the automatic array system(ARIX).When used to cut stone,the cutting amount of the blade with segments made by AME100 is two times that of ordinary tools,with the same diamond concentration.When used to dry cut reinforced concrete,its cutting speed is 10%faster than that of ARIX.Under wet cutting conditions,its service life is twice that of ARIX.By applying the machine vision online inspection system and a special needle jig with a negative pressure system,this study developed a piece of additive manufacturing equipment for efficiently fabricating blade segments with a 3D lattice of diamond grits.

Robot Vision System for Coordinate Measurement of Feature Points on Large Scale Automobile Part

In this paper,we present a robot vision based system for coordinate measurement of feature points on large scale automobile parts.Our system consists of an industrial 6-DOF robot mounted with a CCD camera and a PC.The system controls the robot into the area of feature points.The images of measuring feature points are acquired by the camera mounted on the robot.3D positions of the feature points are obtained from a model based pose estimation that applies to the images.The measured positions of all feature points are then transformed to the reference coordinate of feature points whose positions are obtained from the coordinate measuring machine（CMM）.Finally,the point-to-point distances between the measured feature points and the reference feature points are calculated and reported.The results show that the root mean square error（RMSE） of measure values obtained by our system is less than 0.5 mm.Our system is adequate for automobile assembly and can perform faster than conventional methods.

Camera Calibration for 3D Color Scanning System Using Virtual 3D Model

Instead of traditionally using a 3D physical model with many control points on it, a calibration plate with printed chess grid and movable along its normal direction is implemented to provide large area 3D control points with variable Z values. Experiments show that the approach presented is effective for reconstructing 3D color objects in computer vision system.

Real Objects Understanding Using 3D Haptic Virtual Reality for E-Learning Education

In the past two decades,there has been a lot of work on computer vision technology that incorporates many tasks which implement basic filtering to image classification.Themajor research areas of this field include object detection and object recognition.Moreover,wireless communication technologies are presently adopted and they have impacted the way of education that has been changed.There are different phases of changes in the traditional system.Perception of three-dimensional(3D)from two-dimensional(2D)image is one of the demanding tasks.Because human can easily perceive but making 3D using software will take time manually.Firstly,the blackboard has been replaced by projectors and other digital screens so such that people can understand the concept better through visualization.Secondly,the computer labs in schools are now more common than ever.Thirdly,online classes have become a reality.However,transferring to online education or e-learning is not without challenges.Therefore,we propose a method for improving the efficiency of e-learning.Our proposed system consists of twoand-a-half dimensional(2.5D)features extraction using machine learning and image processing.Then,these features are utilized to generate 3D mesh using ellipsoidal deformation method.After that,3D bounding box estimation is applied.Our results show that there is a need to move to 3D virtual reality(VR)with haptic sensors in the field of e-learning for a better understanding of real-world objects.Thus,people will have more information as compared to the traditional or simple online education tools.We compare our result with the ShapeNet dataset to check the accuracy of our proposed method.Our proposed system achieved an accuracy of 90.77%on plane class,85.72%on chair class,and car class have 72.14%.Mean accuracy of our method is 70.89%.

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.

Recognition of mortar pumpability via computer vision and deep learning

The mortar pumpability is essential in the construction industry,which requires much labor to estimate manually and always causes material waste.This paper proposes an effective method by combining a 3-dimensional convolutional neural network(3D CNN)with a 2-dimensional convolutional long short-term memory network(ConvLSTM2D)to automatically classify the mortar pumpability.Experiment results show that the proposed model has an accuracy rate of 100%with a fast convergence speed,based on the dataset organized by collecting the corresponding mortar image sequences.This work demonstrates the feasibility of using computer vision and deep learning for mortar pumpability classification.

Deep Learning-Based 3D Instance and Semantic Segmentation: A Review

The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation.Segmentation is challenging with point cloud data due to substantial redundancy,fluctuating sample density and lack of apparent organization.The research area has a wide range of robotics applications,including intelligent vehicles,autonomous mapping and navigation.A number of researchers have introduced various methodologies and algorithms.Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I.methods.However,due to the specific problems of processing point clouds with deep neural networks,deep learning on point clouds is still in its initial stages.This study examines many strategies that have been presented to 3D instance and semantic segmentation and gives a complete assessment of current developments in deep learning-based 3D segmentation.In these approaches’benefits,draw backs,and design mechanisms are studied and addressed.This study evaluates the impact of various segmentation algorithms on competitiveness on various publicly accessible datasets,as well as the most often used pipelines,their advantages and limits,insightful findings and intriguing future research directions.

Improved organs at risk segmentation based on modified U‐Net with self‐attention and consistency regularisation

Cancer is one of the leading causes of death in the world,with radiotherapy as one of the treatment options.Radiotherapy planning starts with delineating the affected area from healthy organs,called organs at risk(OAR).A new approach to automatic OAR seg-mentation in the chest cavity in Computed Tomography(CT)images is presented.The proposed approach is based on the modified U‐Net architecture with the ResNet‐34 encoder,which is the baseline adopted in this work.The new two‐branch CS‐SA U‐Net architecture is proposed,which consists of two parallel U‐Net models in which self‐attention blocks with cosine similarity as query‐key similarity function(CS‐SA)blocks are inserted between the encoder and decoder,which enabled the use of con-sistency regularisation.The proposed solution demonstrates state‐of‐the‐art performance for the problem of OAR segmentation in CT images on the publicly available SegTHOR benchmark dataset in terms of a Dice coefficient(oesophagus-0.8714,heart-0.9516,trachea-0.9286,aorta-0.9510)and Hausdorff distance(oesophagus-0.2541,heart-0.1514,trachea-0.1722,aorta-0.1114)and significantly outperforms the baseline.The current approach is demonstrated to be viable for improving the quality of OAR segmentation for radiotherapy planning.

3D时代来临——2006款梅塞德斯奔驰R Class

奔驰汽车公司（MERCEDES—BENZ）在2005年纽约国际车展中首度亮相的全新设计、全新概念R-Class新车已经上市，这款全新的R—Class源自2002年的概念车Vision—R，奔驰公司将之定性为新车种GST（Grand Spots Tourer），并称R—Class包含了运动型房车、旅行车、MPV和SUV等多个不同车种的特性。

双CCD系统在三维面形测量中的应用

讨论了用于直升机着陆激光雷达地形测量系统的双目立体视觉方法,并利用双CCD系统实现了对激光器照射到固定目标表面上任意单点的三维空间坐标测量。首先应用最小二乘法对既定标定点三维空间坐标的测量进行了分析,提出了标定点列的优化原则;然后分析了正交约束条件对测距误差的影响,并建立了双CCD激光测距实验系统。理论分析和实验结果表明,选择经分布优化的标定点列和采用正交约束条件可以使目标点的各向测距误差在总体上得到优化,从而使得双CCD系统在近距测量中有较高的测量精度,适合于三维面形的测量。