3D object reconstruction by point coordinates of 2D line drawings

It is a research subject in computer vision to 3D reconstruction of an object represented by a single 2D line drawing. Previous works on 3D reconstruction from 2D line drawings focus on objects with lines, plane, view, and so on. This paper mainly studies the 3D reconstruction from 2D line drawings. Besides, a new approach is proposed： it is that for the research of the point coordinates of 2D line drawings, so as to achieve the object reconstruction by the reconstruction of point coordinates. The reconstruction process includes： （1） the collection of point coordinates （X,Y） of 2D line drawings; （2） the derivation of mathematical formula about the reconstruction of the point of 2D line drawings, and calculating the corresponding point of the 3D coordinates; （3） the regeneration of 3D graphics with 3D points; （4） analyze error by the proportional of parallel of axonometric projection, in order to prove the accuracy of the method.

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.

Exploring Local Regularities for 3D Object Recognition

In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles（L-MSDA）, localized minimizing standard deviation of segment magnitudes（L-MSDSM）, localized minimum standard deviation of areas of child faces （L-MSDAF）, localized minimum sum of segment magnitudes of common edges （L-MSSM）, and localized minimum sum of areas of child face （L-MSAF）. Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities： L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness.

Scene Visual Perception and AR Navigation Applications

With the rapid popularization of mobile devices and the wide application of various sensors,scene perception methods applied to mobile devices occupy an important position in location-based services such as navigation and augmented reality(AR).The development of deep learning technologies has greatly improved the visual perception ability of machines to scenes.The basic framework of scene visual perception,related technologies and the specific process applied to AR navigation are introduced,and future technology development is proposed.An application(APP)is designed to improve the application effect of AR navigation.The APP includes three modules:navigation map generation,cloud navigation algorithm,and client design.The navigation map generation tool works offline.The cloud saves the navigation map and provides navigation algorithms for the terminal.The terminal realizes local real-time positioning and AR path rendering.

Detecting repetitive structures on building footprints for the purposes of 3D modeling and reconstruction

Repetitive structures of a building share features in terms of geometries and appearance and,therefore,the 3D information for these structures can be transferred from one specification to another for the purpose of 3D modeling and reconstruction once they are identified as repetitive structures.In this paper,a novel approach is proposed for the detection of the repetitive structures specified by the polygons of a building’s footprints.Instead of directly operating on the polygon in 2D space,the polygon is converted into a bend angle function representation in 1D space,whereby an extrusion is represented as a closed polygon intersected by the x-axis and located above it,while an intrusion is represented as a closed polygon below the x-axis.In this way,a polygon of a footprint is decomposed into a number of extrusions and intrusions which can in turn be processed.The task of detecting any repetitive structures specified in a building’s footprints then becomes the task of clustering the intersected polygons in the bend angle function space.The extrusions/intrusions which can be placed in the same clusters can be regarded as repetitive structures.Experiments show that this proposed approach can detect repetitive structures with different sizes,orientations and complexities.

机载激光雷达3维建筑物模型重建的研究进展

机载激光雷达系统已经成为3维建筑物模型快速重建的重要手段。综述了国内外利用机载激光雷达点云进行3维建筑物模型重建的研究方法、进展情况,分析讨论了模型驱动法、数据驱动法和混合法的特点及关键问题。混合法集成了模型驱动方法和数据驱动方法的优点,是该项技术今后发展的重点领域。

基于二维DR的颅面硬组织与软组织拟合的研究

目的 :以数字化X线摄影片(digital radiograph,DR)图像和激光扫描为数据源,三维重建颅面硬组织和软组织,并进行硬软组织的有效拟合,以期为治疗方案确定、手术方法选择和提高医患交流的水平提供平台。方法:在志愿者面部贴定铅点,拍摄头颅正侧位DR片。利用基于二维DR数据的颅面硬组织三维重建系统进行硬组织形变建模,获得颅面部硬组织模型。利用激光扫描数据进行面部软组织三维重建,获得面部软组织三维模型。然后通过铅点坐标的匹配,将颅面硬组织与面部软组织进行拟合。结果:分别重建了能反映真实颅面部组织结构的硬组织三维模型和面部软组织三维模型,并实现了颅面部硬组织与软组织的有效拟合。结论:本研究实现了颅颌面三维结构的有效重建和拟合,为进一步进行正颌手术模拟和容貌预测奠定了基础。拟合结果真实可靠,能够用于临床。

基于LIDAR数据的建筑物提取和三维重建的研究进展

本文阐述了基于LIDAR数据提取建筑物以及对其三维重建的一般过程,着重介绍LIDAR数据中建筑物信息提取的有关算法以及进行三维重建的可行性方法。

A new navigation approach of terrain contour matching based on 3-D terrain reconstruction from onboard image sequence

This article presents a passive navigation method of terrain contour matching by reconstructing the 3-D terrain from the image sequence(acquired by the onboard camera).To achieve automation and simultaneity of the image sequence processing for navigation,a correspondence registration method based on control points tracking is proposed which tracks the sparse control points through the whole image sequence and uses them as correspondence in the relation geometry solution.Besides,a key frame selection method based on the images overlapping ratio and intersecting angles is explored,thereafter the requirement for the camera system configuration is provided.The proposed method also includes an optimal local homography estimating algorithm according to the control points,which helps correctly predict points to be matched and their speed corresponding.Consequently,the real-time 3-D terrain of the trajectory thus reconstructed is matched with the referenced terrain map,and the result of which provides navigating information.The digital simulation experiment and the real image based experiment have verified the proposed method.

Camera localization for augmented reality and indoor positioning:a vision-based 3D feature database approach

The recent fast development in computer vision and mobile sensor technology such as mobile LiDAR and RGB-D cameras is pushing the boundary of the technology to suit the need of real-life applications in the fields of Augmented Reality(AR),robotics,indoor GIS and self-driving.Camera localization is often a key and enabling technology among these applications.In this paper,we developed a novel camera localization workflow based on a highly accurate 3D prior map optimized by our RGBD SLAM method in conjunction with a deep learning routine trained using consecutive video frames labeled with high precision camera pose.Furthermore,an AR registration method tightly coupled with a game engine is proposed,which incorporates the proposed localization algorithm and aligns the real Kinetic camera with a virtual camera of the game engine to facilitate AR application development in an integrated manner.The experimental results show that the localization accuracy can achieve an average error of 35 cm based on a fine-tuned prior 3D feature database at 3 cm accuracy compared against the ground-truth 3D LiDAR map.The influence of the localization accuracy on the visual effect of AR overlay is also demonstrated and the alignment of the real and virtual camera streamlines the implementation of AR fire emergency response demo in a Virtual Geographic Environment.

基于全周多视角的三维重建技术

提出一种基于圆分度台的三维模型拼接技术 ,其核心是把双目立体测量系统安装在步进电机控制的旋转平台上 ,旋转拍摄多视角下的场景影像 ,由所记录的相应角度值 ,通过坐标变换将不同视角场景模型进行三维拼接 .论述了旋转装置的参数标定以及同一视点下不同视角的坐标转换关系 .该方法只需一次标定 ,自动完成测量数据的采集与拼合 ,非常适合于近景场景下的三维数据采集与重建 .实验结果表明 。

单幅高分辨率SAR图像建筑物三维模型重构

提出了一种利用高分辨率SAR图像进行建筑物提取和三维重构的方法.首先,分析了高分辨率SAR图像建筑物产生的电磁散射的类型,给出了不同类型散射区域的后向散射计算方法,并在此基础上给出了一种利用建筑物三维CAD模型进行SAR建筑物特征区域图像仿真的方法;其次,给出了利用建筑物的二次散射结构确定建筑物底部轮廓位置和方向的方法,并提出了一种基于分布密度函数差异的仿真图像迭代匹配方法,进行建筑物高度的反演.仿真SAR图像后向散射系数用来划分建筑物不同的散射区域,通过计算特征区域之间的分布密度函数差异,以取得最大匹配度值的仿真图像对应的检验高度作为建筑物的反演高度;最后,选用了两幅不同屋顶类型的实际机载高分辨率SAR图像进行建筑物提取和三维重构实验,试验结果较为理想,验证了所提方法的可行性和有效性.

有限元法在动物组织模型构建上的应用进展

有限元法是指通过对医学组织图像(CT)的三维重建,将其转换为具有直观立体效果的三维有限元模型,通过对有限元模型进行材料选取、施加载荷、求解等操作,得到有限元组织模型的力学特性,从而更清楚的观察组织的应力、应变过程。该技术可以更好地处理复杂的生物力学问题而被广泛应用于生物医学领域,目前把有限元法和动物组织结合起来的研究逐渐成为了热点。论文对有限元法在动物组织模型构建上的研究前景和应用优势进行综述,为动物组织模型构建方面的研究提供新的方法和思路。

单目平行线约束下的空间点坐标恢复

提出一种从单目像机拍摄的二维投影图像中恢复空间点三维坐标的简化算法.假定摄像机与空间中的一对平行线的位置关系已知,同时摄像机坐标系下平行线所在平面中的任意空间点在像平面中的投影也已知,通过射影几何可得图像坐标到目标点三维空间坐标的转换系数,即坐标转换因数,从而估计出三维空间点的坐标.实验中通过对人体行走轨迹进行估计来测试算法性能,结果表明算法简单有效,计算开销小.

工程图中建立各分视图坐标中心及局部坐标系的算法

已知三视图极值点和三视图划分点,提出了建立各分视图坐标中心及局部坐标系的方法。给出了两种算法:坐标中心通用求解算法和原点取整平移算法。采用ObjectARX的选择集函数及视图变换建立了各分视图的局部坐标系,为后续的图形正误判断或三维重建作了准备。

面向单体建筑精细化建模的无人机三维航线规划

文章提出了一种面向单体建筑精细化建模的无人机三维航线规划方案。该方案使用测绘无人机与单个相机作为单体建筑三维建模信息获取的平台,基于传统航空摄影测量技术,结合近景摄影测量方法,将无人机飞行摄影航线改进为三维环绕式,提高建筑物侧面特征点的提取精度,有效地解决了快速获取单体建筑三维精细化建模所需要的信息这一问题。

考虑重投影尺度因子的特征直线三维重建

文章首先用Plücker坐标表示3D直线;然后用重投影几何误差作为线特征三角法的目标函数,实现算法的最大似然估计;最后用线性和非线性两种方法分别实现空间直线的重建。在重投影时,考虑了每条直线投影时的尺度因子λ,提高了重建精度。真实图像和仿真数据的实验结果表明,该方法能够准确有效地实现直线三维重建。

实时三维重建算法的实现——基于Kinect与单目视觉SLAM的三维重建

作为计算机视觉技术的一个重要分支,基于单目视觉的三维重建技术以其要求简单、成本低廉、易于实现等优点,得到了越来越多的关注。在室内环境下就智能机器人的同步定位以及环境地图创建(SLAM)算法展开了研究,引入RGB-D相机Kinect直接获取3D场景的深度信息,实现了一种基于单目视觉SLAM与Kinect的实时三维重建方法。

超单元体在三轴试验三维离散元模拟中的应用研究

通过干砂三轴试验的三维离散元模拟,探讨使用超单元体这种不规则颗粒单元对试验结果的影响。相对于使用较多的圆球体颗粒,超单元体不仅是对实际砂土颗粒形状更好的拟合,更极大地提高了试件的受力性能,使应力-应变曲线更加贴合实测曲线。通过对模拟结果的细观分析,发现超单元体使试件的细观行为(局部孔隙率、配位数的变化)更为符合实际砂土。随着三轴剪切的进行,试件内部的局部孔隙率的差异性增大,同时由于受力的三向不等性使配位数大幅度降低。

基于激光线扫描的双目立体视觉测量方法研究

为实现工业中汽车、飞机、骨骼和模具外形等物体的三维重建,介绍了基于激光线扫描的双目立体视觉三维测量系统。2个摄像机同时摄取一个扫描激光光条图像,经过图像预处理、光条特征点提取、图像匹配,利用视差计算出光条上所有点的坐标值,即光条处物体的三维信息。通过坐标系之间的转换把各光条统一到同一世界坐标系下,得到整个物体的三维重建。利用该系统对一标定块进行测量,空间距离误差小于0.1 mm,并对鼠标进行扫描测量,得到其表面的三维数据,基本满足逆向工程的要求。