基于智能的配电网电力大数据三维场景可视化分析

配电网电力大数据的三维场景重构是实现数据优化挖掘的关键,提出基于人工智能的配电网电力大数据三维场景可视化分析方法。建立配电网电力大数据三维场景的网格分布结构模型,并进行配电网电力大数据三维场景实时数据监测,根据监测结果进行配电网电力大数据的统计特征分析,对配电网电力大数据三维场景实时数据采用信息融合和模糊层析性分析方法进行信息融合和自适应调度,提取配电网电力大数据的三维可视化分布特征量,采用视觉特征重构技术,实现对配电网电力大数据三维场景可视化重构,在人工智能算法控制下提高电力大数据三维场景可视化重构的精度。仿真结果表明,采用该方法进行配电网电力大数据三维场景可视化重构的精度较高,提高了配电网电力大数据挖掘的效能。     

基于二维激光雷达的自动室内三维重建系统

设计了一个基于二维激光雷达的自动室内三维重建系统.系统的硬件由一套自行设计的基于2D激光雷达的三维扫描系统和一台电脑构成.介绍了系统的软件模块,提出了结合最近点迭代(ICP)和通用多边形裁剪(GPC)的3D平面场景合成方法.ICP能够获得不同采集位置之间的位置变化,以此能将各个不同位置获得的3D场景转换到同一坐标系下.场景合成时的碎平面问题通过GPC方法来解决.实验结果表明:该系统成本低,精度高,能稳定可靠地实现室内场景的自动三维重建.     

一种三维准欧氏重建方法

研究在不必精确知道相机内参数的情况下进行场景的三维重建，充分利用建筑场景的平面信息，假设场景由一些基本平面元素组成，从而自动生成场景的拓扑结构．虽然重建结果从严格意义上仍是射影重建，但已经非常接近欧氏重建结果，文中称之为准欧氏重建，该结果还可以为其它优化算法提供很好的初值．真实图像的实验结果证实了本文算法，重建模型达到了很好的可视效果．     

3D shape reconstruction by using vanishing points

This paper investigates the quantitative reconstruction of the 3D structure of a scene from a line drawing, by using the geometrical constraints provided by the location of vanishing points. The additional information on vanishing points allows the design of an algorithm which has several advantages with respect to the usual approach based on a reduction to linear programming (Sugihara, 1982). These advantages range from a lower computational complexity to error tolerance and exact reconstruction of the 3D-geometry of the objects. These features make the algorithm a useful tool for the quantitative analysis of real-world images, which is useful for several tasks from scene understanding to automatic vehicle guidance     

Qualitative Scene Interpretation Using Planar Surfaces

Our aim is to provide an autonomous vehicle moving into an indoor environment with a visual system to perform a qualitative 3D structure reconstruction of the surrounding environment by recovering the different planar surfaces present in the observed scene.The method is based on qualitative detection of planar surfaces by using projective invariant constraints without the use of depth estimates. The goal is achieved by analyzing two images acquired by observing the scene from two different points of view. The method can be applied to both stereo images and motion images.Our method recovers planar surfaces by clustering high variance interest points whose cross ratio measurements are preserved in two different perspective projections. Once interest points are extracted from each image, the clustering process requires to grouping corresponding points by preserving the cross ratio measurements.We solve the twofold problem of finding corresponding points and grouping the coplanar ones through a global optimization approach based on matching of high relational graphs and clustering on the corresponding association graph through a relaxation labeling algorithm.Through our experimental tests, we found the method to be very fast to converge to a solution, showing how higher order interactions, instead to giving rise to a more complex problem, help to speed-up the optimization process and to reach at same time good results.     

Omnivergent Stereo

The notion of a virtual camera for optimal 3D reconstruction is introduced. Instead of planar perspective images that collect many rays at a fixed viewpoint, omnivergent cameras collect a small number of rays at many different viewpoints. The resulting 2D manifold of rays is arranged into two multiple-perspective images for stereo reconstruction. We call such images omnivergent images, and the process of reconstructing the scene from such images omnivergent stereo. This procedure is shown to produce 3D scene models with minimal reconstruction error, due to the fact that for any point in the 3D scene, two rays with maximum vergence angle can be found in the omnivergent images. Furthermore, omnivergent images are shown to have horizontal epipolar lines, enabling the application of traditional stereo matching algorithms, without modification. Three types of omnivergent virtual cameras are presented: spherical omnivergent cameras,center-strip cameras and dual-strip cameras.     

Preliminary coarse image registration by using straight lines found on them for constructing super resolution mosaics and 3D scene recovery

An algorithm of coarse image registration of a 3D scene taken from different camera perspectives is proposed. The algorithm uses information on geometrical parameters of straight lines found on the images and on distribution of color and/or brightness around these lines. Colors are taken into account by using the fuzzy logic technique. The result of the algorithm operation is a planar projective transformation (planar homography) matching approximately the images. In order to use the technique in algorithms of 3D scene reconstruction, an estimate of size of the window used for searching correspondent points after the coarse image registration is obtained.     

基于结构先验与协同优化的城市场景分段平面重建

在基于图像的城市场景三维重建中,场景分段平面重建算法可以克服场景中的弱纹理、光照变化等因素的影响而快速恢复场景完整的近似结构.然而,在初始空间点较为稀疏、候选平面集不完备、图像过分割质量较低等问题存在时,可靠性往往较低.为了解决此问题,本文根据城市场景的结构特征构造了一种新颖的融合场景结构先验、空间点可见性与颜色相似性的平面可靠性度量,然后采用图像区域与相应平面协同优化的方式对场景结构进行了推断.实验结果表明,本文算法利用稀疏空间点即可有效重建出完整的场景结构,整体上具有较高的精度与效率.     

Learning stratified 3D reconstruction

Stratified 3D reconstruction, or a layer-by-layer 3D reconstruction upgraded from projective to affine, then to the final metric reconstruction, is a well-known 3D reconstruction method in computer vision. It is also a key supporting technology for various well-known applications, such as streetview, smart3D, oblique photogrammetry. Generally speaking, the existing computer vision methods in the literature can be roughly classified into either the geometry-based approaches for spatial vision or the learning-based approaches for object vision. Although deep learning has demonstrated tremendous success in object vision in recent years, learning 3D scene reconstruction from multiple images is still rare, even not existent, except for those on depth learning from single images. This study is to explore the feasibility of learning the stratified 3D reconstruction from putative point correspondences across images, and to assess whether it could also be as robust to matching outliers as the traditional geometry-based methods do. In this study, a special parsimonious neural network is designed for the learning. Our results show that it is indeed possible to learn a stratified 3D reconstruction from noisy image point correspondences, and the learnt reconstruction results appear satisfactory although they are still not on a par with the state-of-the-arts in the structure-from-motion community due to largely its lack of an explicit robust outlier detector such as random sample consensus (RANSAC). To the best of our knowledge, our study is the first attempt in the literature to learn 3D scene reconstruction from multiple images. Our results also show that how to implicitly or explicitly integrate an outlier detector in learning methods is a key problem to solve in order to learn comparable 3D scene structures to those by the current geometry-based state-of-the-arts. Otherwise any significant advancement of learning 3D structures from multiple images seems difficult, if not impossible. Besides, we even speculate that deep learning might be, in nature, not suitable for learning 3D structure from multiple images, or more generally, for solving spatial vision problems.     

Interactive object modelling based on piecewise planar surface patches

Detecting elements such as planes in 3D is essential to describe objects for applications such as robotics and augmented reality. While plane estimation is well studied, table-top scenes exhibit a large number of planes and methods often lock onto a dominant plane or do not estimate 3D object structure but only homographies of individual planes. In this paper we introduce MDL to the problem of incrementally detecting multiple planar patches in a scene using tracked interest points in image sequences. Planar patches are reconstructed and stored in a keyframe-based graph structure. In case different motions occur, separate object hypotheses are modelled from currently visible patches and patches seen in previous frames. We evaluate our approach on a standard data set published by the Visual Geometry Group at the University of Oxford [24] and on our own data set containing table-top scenes. Results indicate that our approach significantly improves over the state-of-the-art algorithms.     

Obstacle detection based on qualitative and quantitative 3Dreconstruction

Three different algorithms for mobile robot vision-based obstacle detection are presented in this paper each based on different assumptions The first two algorithms are qualitative in that they return only yes/no answers regarding the presence of obstacles in the field of view; no 3D reconstruction is performed. They have the advantage of fast determination of the existence of obstacles in a scene based on the solvability of a linear system. The first algorithm uses information about the ground plane, while the second only assumes that the ground is planar. The third algorithm is quantitative in that it continuously estimates the ground plane and reconstructs partial 3D structures by determining the height above the ground plane of each point in the scene. Experimental results are presented for real and simulated data, and the performance of the three algorithms under different noise levels is compared in simulation. We conclude that in terms of the robustness of performance, the third algorithm is superior to the other two     

Structure from Motion: Beyond the Epipolar Constraint

The classic approach to structure from motion entails a clear separation between motion estimation and structure estimation and between two-dimensional (2D) and three-dimensional (3D) information. For the recovery of the rigid transformation between different views only 2D image measurements are used. To have available enough information, most existing techniques are based on the intermediate computation of optical flow which, however, poses a problem at the locations of depth discontinuities. If we knew where depth discontinuities were, we could (using a multitude of approaches based on smoothness constraints) accurately estimate flow values for image patches corresponding to smooth scene patches; but to know the discontinuities requires solving the structure from motion problem first. This paper introduces a novel approach to structure from motion which addresses the processes of smoothing, 3D motion and structure estimation in a synergistic manner. It provides an algorithm for estimating the transformation between two views obtained by either a calibrated or uncalibrated camera. The results of the estimation are then utilized to perform a reconstruction of the scene from a short sequence of images.The technique is based on constraints on image derivatives which involve the 3D motion and shape of the scene, leading to a geometric and statistical estimation problem. The interaction between 3D motion and shape allows us to estimate the 3D motion while at the same time segmenting the scene. If we use a wrong 3D motion estimate to compute depth, we obtain a distorted version of the depth function. The distortion, however, is such that the worse the motion estimate, the more likely we are to obtain depth estimates that vary locally more than the correct ones. Since local variability of depth is due either to the existence of a discontinuity or to a wrong 3D motion estimate, being able to differentiate between these two cases provides the correct motion, which yields the least varying estimated depth as well as the image locations of scene discontinuities. We analyze the new constraints, show their relationship to the minimization of the epipolar constraint, and present experimental results using real image sequences that indicate the robustness of the method.     

采用去抖动模糊算法的稠密三维重建

针对无人机在航拍大场景对象进行三维重建时因抖动产生的图像模糊现象,以及二维图像序列经运动恢复结构SFM后得到的点云较为稀疏,可视化差等不足,采用去抖动模糊算法恢复模糊图像的原始图像信息,然后在运动恢复结构的基础上进行基于点云的稠密三维重建,最后对稠密重建后的点云进行泊松表面重建以得到表面致密、均匀的三维模型。实验结果表明,去抖动模糊算法可以有效地提高图像的质量,大场景对象经过基于点云的稠密三维重建后得到的重建效果逼真,可视化强。     

二次联合稀疏表示和低秩近似的浅浮雕优化

目的 针对低质量浅浮雕表面的噪声现象,提出一种二次联合局部自适应稀疏表示和非局部低秩矩阵近似的浅浮雕优化算法。方法 本文方法分两个阶段。第1阶段,将浅浮雕灰度图划分成大小相同的数据块,提取边界块并进行去噪,分别对数据块进行稀疏表示和低秩近似处理。一方面,通过字典学习获得过完备字典和稀疏编码;另一方面,利用K均值聚类算法（K-means）将事先构建的外部字典库划分成k类,从k个簇中心匹配每个数据块的相似块并组成相似矩阵,依次进行低秩近似和特征增强处理。最后通过最小二乘法求解,重建并聚合新建数据块以得到新的高度场。第2阶段与第1阶段的结构相似,主要区别在于改用重建高度场的非局部自身相似性来实现块匹配。结果 在不同图像压缩率下（70%,50%,30%）,对比本文方法与BM3D（block-matching and 3D filtering）、WNNM（weighted nuclear norm minimization）、STROLLR（sparsifying transform learning and low-rank）、TWSC（trilateral weighted sparse coding）4个平滑降噪方法的浅浮雕重建结果,发现BM3D和STROLLR方法的特征保持虽好,但平滑效果较差,WNNM方法出现模型破损现象,TWSC方法的平滑效果比BM3D和STROLLR方法更好,但特征也同时被光顺化。阴影恢复形状法（shape from shading,SFS）是一种基于图像的3D建模法,但是其重建结果比较粗糙。相比之下,本文方法生成的浅浮雕模型更加清晰直观,在浅浮雕的特征增强和平滑去噪方面都展现出更好的性能。结论 本文综合数据块的局部稀疏性和数据块之间的非局部相似性对粗糙的浅浮雕模型进行二次高度场重建。本文方法有效改善了现有浅浮雕模型的质量,提高了模型的整体视觉效果,为浅浮雕的优化提供了新方法。     

Exposure Fusion for Time‐Of‐Flight Imaging

This work deals with the problem of automatically choosing the correct exposure (or integration) time for time‐of‐flight depth image capturing. We apply methods known from high dynamic range imaging to combine depth images taken with differing integration times in order to produce high quality depth maps. We evaluate the quality of these depth maps by comparing the performance in reconstruction of planar textured patches and in the 3D reconstruction of an indoor scene. Our solution is fast enough to capture the images at interactive frame rates and also flexible to deal with any amount of exposures.     

基于多模态推理图神经网络的场景文本视觉问答模型

文本阅读能力差和视觉推理能力不足是现有视觉问答(visual question answering, VQA)模型效果不好的主要原因,针对以上问题,设计了一个基于图神经网络的多模态推理(multi-modal reasoning graph neural network, MRGNN)模型。利用图像中多种形式的信息帮助理解场景文本内容,将场景文本图片分别预处理成视觉对象图和文本图的形式,并且在问题自注意力模块下过滤多余的信息;使用加入注意力的聚合器完善子图之间相互的节点特征,从而融合不同模态之间的信息,更新后的节点利用不同模态的上下文信息为答疑模块提供了更好的功能。在ST-VQA和TextVQA数据集上验证了有效性,实验结果表明,相比较此任务的一些其他模型,MRGNN模型在此任务上有明显的提升。     

基于多模态多级特征聚合网络的光场显著性目标检测

现有基于深度学习的显著性检测算法主要针对二维RGB图像设计,未能利用场景图像的三维视觉信息,而当前光场显著性检测方法则多数基于手工设计,特征表示能力不足,导致上述方法在各种挑战性自然场景图像上的检测效果不理想。提出一种基于卷积神经网络的多模态多级特征精炼与融合网络算法,利用光场图像丰富的视觉信息,实现面向四维光场图像的精准显著性检测。为充分挖掘三维视觉信息,设计2个并行的子网络分别处理全聚焦图像和深度图像。在此基础上,构建跨模态特征聚合模块实现对全聚焦图像、焦堆栈序列和深度图3个模态的跨模态多级视觉特征聚合,以更有效地突出场景中的显著性目标对象。在DUTLF-FS和HFUT-Lytro光场基准数据集上进行实验对比,结果表明,该算法在5个权威评估度量指标上均优于MOLF、AFNet、DMRA等主流显著性目标检测算法。     

纹理映射中的平面校正技术研究

为了快速实时地进行由平面组成的结构景物的3D建模问题,文中介绍了一种在进行图像3D重构时纹理映射中的平面校正方法。介绍了三角形模型在图像处理、图形绘制、虚拟现实等技术中的重要作用。从射影几何的角度出发,给出了从两幅视图进行景物三维重构的分层重构方法。在已知欧氏重构即摄像机内参数的基础上,介绍一种基于标定的平面射影失真矫正方法。通过此方法,将矫正过的纹理映射到欧式点重构结构中,得到景物的3D模型。经实验验证,这种方法在处理由平面组成的景物的3D重构中是实时有效的。