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摄像机自标定和三维重建中若干问题的研究
其他题名Some Problems in Camera Self-calibration and 3D Reconstruction
孟晓桥
学位类型工学硕士
导师胡占义
2000-12-01
学位授予单位中国科学院自动化研究所
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
摘要本文作者在模式识别国家重点实验室机器人视觉组攻读硕十学位期间,主要从事三维立 体视觉方面的研究,具体内容包括摄像机自标定、特征点的白动匹配、三维重建等。主要工 作可以归纳为以下三个方面: 1.基于圆环点的摄像机自标定方法研究。该方法使用一种打印有圆和通过圆心的若干 条直线组成的新型模板,仅要求摄像机在三个(或三个以上)不同方位摄取该模板 的图像,即可线性求解全部摄像机内参数。该方法的优越性在于:(1)不需要建立 图象间的匹配关系:(2)不需要确定圆心、圆的半径等物体度量。整个定标过程可 以自动进行,非常适合非视觉专业人员使用。我们同时还讨论了其它几种具有标定 可能的模板。 2.图象间特征点的自动匹配算法研究和匹配软件的编制。在研究利改进文献中现有方 法的基础上,我们搭建了一个自动匹配软件。该匹配软什在借鉴和改进现有匹配方 法的基础上实现了两种匹配策略:(一)当图象视差较小时,该软件可以实现子像素 级的自动匹配;(二)当视差较大造成自动匹配有困难时,允许先由人工选取若干种 子匹配对以恢复出极线约束,再由自动匹配算法完成剩下的匹配任务。目前,该匹 配软件已在机器人视觉组的三维重建、IBR等课题中得到应用,并取得了良好的效 果 3.初步讨论了一个三维重建演示系统。在对摄像机进行标定的基础上,首先使用上面 的匹配软件确定图象序列两两图象之间图象点的匹配关系,在此基础上利用两幅图 的SFM算法计算出两幅图所确定的三维结构。经过数据融合后,所有这些三维结构 被统一到同一个三维坐标系下,从而得到大量的离散的三维点。最后,为解决物体 表面的重建和纹理的粘贴,我们采用了人工干预与自动三角划分相结合的方法。
其他摘要Over the last two years I spent in the Robot Vision Group of NLPR as a master student, my efforts have been primarily concentrated on camera self-calibration, matching and 3 D reconstruction. The main work of this thesis consists of the following three parts: 1. Camera Self-calibration Based on Circular Points. A new self-calibration technique is proposed which uses a new type of calibration pattern composed of a circle with lines passing through its center. The proposed technique only requires the camera to observe the calibration pattern at a few(at least three) different orientations, then all the intrinsic parameters can be determined linearly. The main points of our technique are. (1) It need not to establish the correspondence between points of the calibration pattern and its projected image;(2)It need not know the circle center and radius. The calibration process can be totally automatic. Hence it is especially convenient to those people who are not familiar with computer vision. Besides, we also list a catalogue of other possible calibration patterns. 2. Image Point Correspondence and Its Implementation. Based on recent literatures on point correspondence, we have developed a matching software. This software offers two matching facilities for a given pair of images. (1) When the baseline between the two viewpoints is short, it can automatically establish the point correspondence to sub-pixel accuracy. (2) In wide baseline cases, automatic correspondence becomes extremely difficult, the software permits manual intervention to manually select several seeding correspondences to recover the epipolar geometry, then the subsequent corresponding process proceeds automatically. Now the software has been successfully used in 3D reconstruction, IBR and other projects in our group. 3. Implementation of a 3D Reconstruction Demonstration System. First, the camera was calibrated separately, then the image point correspondence was done using the above matching software. After that, the sparse 3D points were obtained image-pair-wisely with a SFM algorithm, and followed by a data fusion process to transform the different reconstructed points into a same Euclidean coordinate system. And finally, by some manual intervention but a largely automatic process of dense matching, surface reconstruction and texture mapping were carried out. This demonstration system seems to perform nicely.
馆藏号XWLW588
其他标识符588
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/7322
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
孟晓桥. 摄像机自标定和三维重建中若干问题的研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2000.
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