【作者】 万艳丽；

【导师】 苗振江；

【作者基本信息】 北京交通大学 ， 信号与信息处理， 2012， 博士

【摘要】 随着互联网技术的迅速发展,网络中图像数据库的规模不断扩大,人们开始利用网上提供的图像资源构建各种真实场景的模型,这也使得三维建模变得更加有意义。但是互联网上的图像来源不同,再加上外界噪音和遮挡因素的影响,使得既使是对同一场景拍摄的图像也具有显著的差异。因此,网络中的图像资源给三维建模提供便利的同时,也给重建工作带来更大的挑战。本文以基于互联网图像资源实现户外开放性场景的多目重构为研究背景,紧紧围绕重建中涉及的关键技术开展研究工作,在深入的学习和分析现有的相关文献和算法的基础上在下面几个方面取得了一些创新性的研究成果：(1)提出一个新的结合灰度信息和颜色信息的局部不变描述符HRCRD的构建方法。该描述符由基于灰度Haar小波响应构建的子描述符和基于颜色比率不变模型构建的子描述符组成。其中对所提出的颜色比率不变模型,从理论上和实验上均证明了在视点变化、光照方向变化、光照强度变化和光照颜色变化等各种变化条件下能保持较好的不变性。HRCRD描述符不仅具有较快的描述速度,而且提高了现有描述符的独特性和鲁棒性。2)提出一种新的匹配代价函数,它基于颜色分量、方向分量和距离分量对传统的匹配代价函数进行加权,大大降低误匹配率。本文还提出了一种基于仿射变换优化模型的密集匹配算法,结合新的匹配代价函数,使传统的基于支持窗的密集匹配算法适用于宽基线图像的情况,且使密集匹配的精度达到亚像素级。3)针对互联网图像间尺度和基线对自定标算法精度的影响,提出了基于邻域视图选择方案的匹配点跟踪算法,使准密集匹配点在每个图像对应的邻域视图中快速精确的跟踪；针对参数优化过程中,由于输入图像的个数和3D点的个数较多致使全局优化开销变得非常大,甚至可能优化失败的情况,提出了一种两层迭代优化算法。内层迭代对相机参数和3D准密集点参数采用全局和局部相结合的优化策略。外层迭代基于重投影误差对外点进行剔除,降低外点对定标精度的影响。本文在多组图像集中对所提出的算法进行了验证,表明提出的两层迭代算法不但可以获得较密集的3D点云,而且比传统的全局优化算法获得更好的精度。4)针对互联网中的图像具有规模大、尺度范围大、分辨率高低不同等特点,提出将输入图像分级分组和视图选择：场景级图像预分组、图像级视图选择以及点级视图选择,面向重构中不同阶段的问题,较高效地组织图像。其中场景级预分组算法首先采用全局GIST特征对图像粗分组,剔除不必要的分组；然后采用局部HRCRD特征和两视图几何约束对分组进一步求精(筛选和合并),剔除组内具有较低相关性的图像并根据不同的视点范围和尺度范围对图像进行组内再分组(细化),有效的组织图像。在以上各部分的基础上,搭建了一个多视点多尺度三维密集点云重构系统,将前面提出的算法集成到统一的平台。通过实验证明,该系统既可以实现户外多尺度场景的密集点云重构,也可以实现单一尺度场景的密集点云重构。更多还原

【Abstract】 With the development of the Internet, the image database on the Internet becomes more and more abundant. More and more researchers start to reconstruct the real scene model using the Internet images. This makes image-based modeling more meaninful. Since the images on the Internet usually come from different cameras, they usually have different noise levels and occlusions. This leads to the different appearance of the photos, even they are captured at the same scene. Although the image resource on the Internet brings some conveniences, it brings more challenges for reconstruction. This paper focuses on the key techniques involved in3D reconstruction from Internet images, and several novel and practically useful algorithms are proposed as follows:(1) A novel local invariant descriptor HRCRD is proposed by combining the intensity and color information. This descriptor is built based on two sub-descriptors:Haar wavelet response sub-descriptor, and color ratio invariant sub-descriptor. The color ratio invariant model is invariant to the changes of viewing direction, highlights, illumination direction, illumination intensity, and illumination color. This descriptor not only improves the describing speed of most existing descriptors, but also improves the discriminative power and robustness.(2) A new matching cost function is proposed by weighting the traditional function using the color component, direction component, and the distance component. This greatly reduces the error matching. It is further integrated with a proposed affine transformation based dense matching function to improve the matching accuracy at the sub-pixel level.(3) In order to eliminate the negative impact of the variant scale and baseline of the internet images, a neighboring view selection strategy is proposed to quickly and accurately track matching points in multi-views. A two layer iteration optimization algorithm is proposed to resolve the problems that the optimization process in the camera calibration cost high, or even fail due to the large amount of input images and3D quasi-dense points. In the inner layer, local photometric consistency and a global objective function are used to optimize3D quasi-dense points and camera parameters respectively, and the two processes switch iteratively. In the outer layer, the outliers are discarded by reprojection error in order to reduce the negative impact of the outliers. The proposed algorithms are tested with several image sets. The experimental results demonstrate that our algorithm performs better than SBA algorithm. In addition, our algorithm has more superiority when the number of images is small.(4) In order to deal with the internet images that have the characteristics of large amount, large scale invariant, and large resolution invariant, an input image grouping and view selection algorithm is proposed. It has three level, the scene level image pre-grouping, image level view selection, and point level view selection. For the different stages of the reconstruction, the images can be arranged more effectively. The scene level image pre-grouping algorithm employs the global GIST features to roughly group the image, and eliminate the unnecessary groups. Then the local HRCRD features and epipolar geometry are employed to refine the groups. The images with low correlations to other images in the group will be discarded, and the remaining images will be further grouped according to their scales and views.On the basis of the feasibility and effectiveness of above methods and algorithms, we develop a multi-view and multi-scale3D dense point cloud reconstruction system which integrating all the algorithms proposed in this paper. Further experiments demonstrate that this system not only can reconstruct the point cloud for the outdoor multi-scale scenes, but also be applicable for the single scale scenes.更多还原