【作者】 彭科举；

【导师】 刘云辉；

【作者基本信息】 国防科学技术大学 ， 信息与通信工程， 2012， 博士

【摘要】 计算机视觉主要研究如何利用计算机实现人的视觉功能，即利用二维投影图像实现对客观世界三维场景的感知、识别和理解。随着计算机技术的迅速发展，人们对三维模型的需求越来越多。基于序列图像的三维重建方法具有成本低廉、操作简单、真实感强等优点，已成为计算机视觉领域的研究热点之一。本文研究了基于序列图像的三维重建的相关算法，系统总结了作者在基于两幅图像的三维重建、基于多幅图像的三维重建、稠密重建以及三维重建的实际应用等方面所做的研究和取得的成果。(1)提出了一种基于SIFT(Scale Invariant Feature Transform，尺度不变特征变换)特征和角点特征相结合的三维重建算法。本文算法的基本思想是：SIFT特征点匹配比较准确但是很多匹配点对并不是三维重建所需要的点，而角点更能表达物体的真实形状，在SIFT特征点的基础上加入物体的角点特征，二者取长补短，使得重建结果在细节上更加突出，同不利用角点特征计算出的结果相比，二者结合后的重建物体真实感更强，更接近真实物体。(2)提出了一种基于射影深度求解及简化ICP(Iterative Closest Point，迭代最近点)算法的序列图像三维重建算法。本文从几何意义的角度进行考虑，根据重构的多义性原理，得出一个关于射影深度及重建三维点比例系数之间关系的推论并由推论的证明过程实现了基于序列图像的三维重建。同时，为了避免误差累积，提出用简化迭代最近点算法校正重建结果。同基于顺序方法的序列图像三维重建相比，由于本文算法相当于对基于两幅图像的重建结果进行叠加，每两幅图像的重建结果之间不受前面结果的影响，从而有效避免了误差累积问题；与传统的基于矩阵分解的序列图像三维重建算法的不同之处在于本文算法不要求重建点在所有图像中均可见，从而可以在保证有足够多匹配点的情况下重建出目标的完整形状。(3)提出利用稠密点进行网格化所形成的三角形的平均周长剔除误匹配，在特征点数量比较多的情况下，避免了重复计算比较耗时的基础矩阵，在不影响重建质量的前提下，有效加快了重建结果优化速度。本文研究了基于区域增长的稠密匹配算法，利用优化后的SIFT匹配点作为区域增长的种子点，大大提高了稠密匹配的准确度。本文还推导了序列图像相对同一参考坐标系的投影矩阵求解公式，从而可以将本文算法与目前效果最好的多视图重建算法相结合，解决了拍摄过程中由于光照不一致而影响重建效果的问题。(4)提出考虑相机安装误差情况下基于单目摄像机的车辆行进方向与道路之间夹角的检测方法，同时，为了便于对比，根据三维重建原理利用双目视觉方法完成了对车辆行进方向与道路之间的夹角检测。本文利用机器视觉知识运用几何方法计算出了实际偏离角度，在假设相机与地面成一定角度的情况下计算了车辆行进方向与道路的夹角，并在实际拍摄的场景中验证了算法的有效性。本文算法的突出优势是相机距地面的高度以及俯仰角和水平偏转角不需要实际测量，完全由计算得到，从而进一步减少了测量误差。更多还原

【Abstract】 Computer vision mainly studies how to use computers to implement the humanvisual function, namely, by using the two-dimensional projective images to achieve theperception, recognition and understanding of the three-dimensional objective worldseene. With the rapid development of computer technologies，people has more and moredemand for3D models.3D reconstruction based on image sequences has become one ofthe hot research topics in the field of computer vision because it has the advantages ofbeing low cost, simple and realistic. This thesis studied the correlative algorithms of3Dreconstruction using image sequences and systematically summarized the studies andachievements of the author in3D reconstruction based on two images,3Dreconstruction based on image sequences, dense reconstruction and the practicalapplication of3D reconstruction.(1) A3D reconstruction algorithm based on SIFT and corner detector is presentedin this paper. The basic idea of the proposed algorithm is: SIFT feature points areaccurate, but many of them are not what needed in reconstruction. Corners can betterexpress the basic shape of objects. By combining the SIFT feature points with the Harriscorners it is possible to obtain more vivid and detailed3D models. Compared with themethod without corners, reconstruction results of the proposed method is much closer tothe real object.(2) A novel3D reconstruction method using image sequences based on projectivedepth and the simplified Iterative Closest Point (ICP) is proposed. This paper firstpresents a corollary about the relation between the projective depth and3Dreconstruction points based on the ambiguity of reconstruction and then gives thedetailed proof. It proposes a new algorithm for3D construction from a sequence ofimages. In order to avoid accumulation of errors, this algorithm modifies thereconstruction results based on a simplified ICP algorithm. Compared with existingsequential algorithms, our algorithm can reduce the impact of accumulated errorsbecause the reconstruction process is equal to the pileup of the reconstruction resultsbased on two images. Moreover, compared with methods using Measurement MatrixFactorization, our algorithm does not require the feature points to be visible in all theimages. Therefore it is possible to reconstruct the whole shape of the object withsufficient corresponding points by the proposed algorithm.(3) For dense points reconstruction, we developed a new method that optimizes the3D points by using the average girth of the triangles obtained from triangulation. It iscomputationally expensive to eliminate false matches with the fundamental matrix whenthere are a large number of points. Our algorithm can effectively accelerate theoptimization speed of the reconstruction result without affecting the reconstruction quality. We also studied the dense matching algorithm based on region growing andused the sparse points obtained from the SIFT feature matching algorithm andoptimized by the fundamental matrix as the seed points of the dense matching algorithm.As a result, the accuracy of the dense matching has been greatly improved. Wecalculated the projective matrices of the image sequence relative to the same referencecoordinate system according to this paper’s algorithm, the projective matrices were usedas the input of the up to date multi-view stereo algorithm, and the influence of photoconsistency had been avoided.(4) To cope with the installation error of the camera, we developed an algorithm tocalculate the angle between the moving orientation of the vehicle and the drivewaybased on monocular vision using geometrical method. In order to facilitate comparison,we also achieved the angle detection between the moving orientation of the vehicle andthe driveway based on binocular vision according to the theory of3D reconstruction.The angle between the moving orientation of the vehicle and the driveway wascalculated with a hypothesis that there was an angle between the optical axis of thecamera and the ground, the actual deviation angle was also computed. Images capturedfrom the real scene validated the accuracy of our method. The prominent advantage ofthe proposed algorithm is that we do not need to measure the camera height above theground and the pitch angle and also the deflection angle, all the value can be obtainedfrom calculation, which can further decrease the measure error.更多还原