基于多视图的三维景物重建技术研究
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摘要
随着计算机技术的不断发展,利用计算机视觉自动获取景物的三维信息已在社会生产、生活的各个方面显示出越来越重要的地位和作用。由于多幅图像包含更多的景物信息,扩大了视场范围,因此利用多幅图像进行三维重建可以完成单幅视图无法完成或难以完成的工作,且基于多视图的三维重建具有成本低廉、操作方便、实现简单等优点,已经成为计算机视觉研究领域热点之一。
基于多视图的三维景物重建研究包括特征点提取与匹配、基本矩阵估计、摄像机自标定、三维景物重建、表面稠密化重建等多项技术。本文将对基本矩阵估计、摄像机自标定、三维景物重建等关键技术进行重点研究,主要研究内容如下：
(1)基本矩阵估计
为了提高基本矩阵估计精度,提出了一种基于改进MLESAC算法的基本矩阵估计方法。该方法首先根据对极距离值选择对极距离小的原始数据,采用RANSAC算法剔除错误匹配点,再用LM算法最小化Sampson误差来估计基本矩阵的初始值；根据获得的对极几何关系,附加约束条件检验匹配集,以便进一步提高匹配集的精度；最后,利用MLESAC算法对新的匹配集进行迭代求解,从而得到精确的基本矩阵。实验结果表明,该方法大大提高了估计精度,稳定性也比较好。
(2)摄像机自标定
针对传统的摄像机自标定方法精度低的问题,提出了一种基于GA-PSO算法的摄像机自标定方法。该方法首先通过对基本矩阵进行奇异值分解,得到简化的Kruppa方程,建立基于Kruppa方程的优化代价函数；然后通过利用GA-PSO算法求优化代价函数的最小值来完成摄像机自标定过程。由于GA-PSO算法将遗传算法和粒子群算法进行了融合和优势互补,所以可以提高计算精度。实验结果表明,该自标定方法可以大大提高自标定的精度。
(3)三维景物重建
针对传统的L。范数重建方法精度低、对局外点敏感以及效率低等问题,提出一种基于L1与空间点分类(简称‘‘L1-SPC”)的三维景物重建方法。首先采用L1方法剔除局外点。剔除局外点可以提高重建精度,且由于在后面的三维重建过程中不用对局外点的数据进行三维重建计算,因此减少了计算时间。再将空间点分为两视图可见空间点和多视图可见空间点两类。对第一类空间点采用高精度的最优三角形法进行重建,第二类空间点则采用改进的L∞范数方法进行重建。改进的L。范数方法可以不断调整二分搜索的上下界,减少了迭代次数,提高了计算效率。实验结果表明,基于L1-SPC方法的三维景物重建方法精度高,效率也较高。
(4)三维景物中平表面重建
针对使用传统的三维景物重建方法用于三维景物中平表面重建而出现的精度低等问题,提出了两种基于多视图的三维景物中平表面重建模型：基于最小化反投影误差的平表面重建重建模型和基于最小化转移误差的平表面重建重建模型。第一种模型利用反投影线应与空间平面相交且交于一点,从而将误差转移到空间平面上进行最小化反投影误差：第二种模型利用二维空间平面与二维图像平面之间的单应转移关系,从而将误差转移到空间平面上最小化转移误差。这两种模型都采用智能算法(GA算法)进行优化求解,从而获得平表面重建结果。实际上,两种平表面重建方法的基本原理相同,只是计算复杂度不同。实验结果表明,两种平表面重建方法的精度基本一致,而平表面重建的精度大大提高。
论文最后作了总结,阐述了本研究课题的创新点及主要研究成果,并对课题中需要改进之处和有待提高的地方提出了展望。
As the developing of computer technologies, obtaining realistic3D scene information automatically plays more and more important role in our social production and life. Because multiple view contain more information and expand the scope of the field, so3D reconstruction based on multiple view can complete the work which a single view can not complete or complete difficultly.3D reconstruction based on multiple view is a hotspot research aspect in computer vision fields since it is inexpensive, convenient and simpler.
There are many key techniques of3D reconstruction based on multiple view such as feature detection and matching, fundamental matrix estimation, camera self-calibration,3D scene reconstruction, dense surface reconstruction and so on. The thesis is focused on fundamental matrix estimation, camera self-calibration and3D scene reconstruction. The main research work are as follows:
(1) Fundamental matrix estimation
In order to improve the accuracy of fundamental matrix estimation, an improved MLESAC algorithm is proposed. Firstly, according to the distances between the matching points and the corresponding epipolar lines, the superior correspondences are chosen, random sample consensus is adopted to sample the superior correspondences, and then the initial fundamental matrix is obtained using the Levenberg-Marquardt algorithm to minimize Sampson error; Secondly, according to the epipoplar geometry and adding constraints to detect matching points set, then the accuracy of matching points set is further improved; Finally, the accurate fundamental matrix is computed iteratively by using MLESAC. Experimental results show that the accuracy of our algorithm is improved, and the stability is good too.
(2) Camera self-calibration
In order to improve the accuracy of camera self-calibration. We propose a camera self-calibration method based on GA-PSO Algorithm. Firstly, the simplified Kruppa equations based on the SVD of the fundamental matrix is converted into the optimized cost function. Secondly, the minimum value of the optimized cost function is calculated by GA-PSO, then the intrinsic parameters of the camera are obtained. Because the GA-PSO algorithm combines with the advantages of genetic algorithm and particle swarm optimization, the accuracy of computation is improved. The experimental results show that the accuracy of camera self-calibration is improved.
(3)3D scene reconstruction
In view of the problems such as low accuracy of traditional reconstruction method by minimizing L∞norm, being sensitive to outliers and low efficiency,3D scene reconstruction based on L, and space point classification(marked as" L1-SPC") is presented. Firstly, L1approach is used to remove the outliers. Because we don't need to calculate the outliers in the following3D reconstruction, the accuracy of reconstruction can be improved and the computation time is reduced. Secondly, the scene points are divided into the two classes, the first class is the scene points which are visible in two-view; the second class is the scene points which are visible in more than two-view. The optimal triangulation method is used for the first class; The improved L∞-norm method is used for the second class. The gap between the upper and lower bound of bisection is kept as small as possible in the improved L∞-norm method, so the computation time is decreased. The experimental results show that the3D reconstruction based on L1-SPC method is higher accuracy and more efficient.
(4)3D scene plane reconstruction
In view of the problems such as low accuracy of3D scene plane reconstruction by using tranditional3D reconstruction method,3D scene plane reconstruction of two new model based on multiple view are presented. One is the model of scene plane reconstruction based on minimizing the reverse projection error, the other is the model of scene plane reconstruction based on minimizing the transfer error. The first model considering the knowledge that the reverse projection lines not only intersect but also meet in a scene plane, so minimizing the reverse projection error in the scene plane. The second model uses the transfer relation between the image plane and the scene plane, so minimizing the transfer error in the scene plane. At last, the optimized value is computed by GA. The basic principle of two methods is same, and the difference between them is the computational complexity. The experimental results show that the accuracy of two methods is almost same and the accurancy of3D scene plane reconstruction is improved greatly.
At the end of this dissertation, the main research is summarized. It makes out the main innovations and research achievements, and also points out the problems and issues which need to further research.
基于多视图的三维景物重建研究包括特征点提取与匹配、基本矩阵估计、摄像机自标定、三维景物重建、表面稠密化重建等多项技术。本文将对基本矩阵估计、摄像机自标定、三维景物重建等关键技术进行重点研究,主要研究内容如下：
(1)基本矩阵估计
为了提高基本矩阵估计精度,提出了一种基于改进MLESAC算法的基本矩阵估计方法。该方法首先根据对极距离值选择对极距离小的原始数据,采用RANSAC算法剔除错误匹配点,再用LM算法最小化Sampson误差来估计基本矩阵的初始值；根据获得的对极几何关系,附加约束条件检验匹配集,以便进一步提高匹配集的精度；最后,利用MLESAC算法对新的匹配集进行迭代求解,从而得到精确的基本矩阵。实验结果表明,该方法大大提高了估计精度,稳定性也比较好。
(2)摄像机自标定
针对传统的摄像机自标定方法精度低的问题,提出了一种基于GA-PSO算法的摄像机自标定方法。该方法首先通过对基本矩阵进行奇异值分解,得到简化的Kruppa方程,建立基于Kruppa方程的优化代价函数；然后通过利用GA-PSO算法求优化代价函数的最小值来完成摄像机自标定过程。由于GA-PSO算法将遗传算法和粒子群算法进行了融合和优势互补,所以可以提高计算精度。实验结果表明,该自标定方法可以大大提高自标定的精度。
(3)三维景物重建
针对传统的L。范数重建方法精度低、对局外点敏感以及效率低等问题,提出一种基于L1与空间点分类(简称‘‘L1-SPC”)的三维景物重建方法。首先采用L1方法剔除局外点。剔除局外点可以提高重建精度,且由于在后面的三维重建过程中不用对局外点的数据进行三维重建计算,因此减少了计算时间。再将空间点分为两视图可见空间点和多视图可见空间点两类。对第一类空间点采用高精度的最优三角形法进行重建,第二类空间点则采用改进的L∞范数方法进行重建。改进的L。范数方法可以不断调整二分搜索的上下界,减少了迭代次数,提高了计算效率。实验结果表明,基于L1-SPC方法的三维景物重建方法精度高,效率也较高。
(4)三维景物中平表面重建
针对使用传统的三维景物重建方法用于三维景物中平表面重建而出现的精度低等问题,提出了两种基于多视图的三维景物中平表面重建模型：基于最小化反投影误差的平表面重建重建模型和基于最小化转移误差的平表面重建重建模型。第一种模型利用反投影线应与空间平面相交且交于一点,从而将误差转移到空间平面上进行最小化反投影误差：第二种模型利用二维空间平面与二维图像平面之间的单应转移关系,从而将误差转移到空间平面上最小化转移误差。这两种模型都采用智能算法(GA算法)进行优化求解,从而获得平表面重建结果。实际上,两种平表面重建方法的基本原理相同,只是计算复杂度不同。实验结果表明,两种平表面重建方法的精度基本一致,而平表面重建的精度大大提高。
论文最后作了总结,阐述了本研究课题的创新点及主要研究成果,并对课题中需要改进之处和有待提高的地方提出了展望。
As the developing of computer technologies, obtaining realistic3D scene information automatically plays more and more important role in our social production and life. Because multiple view contain more information and expand the scope of the field, so3D reconstruction based on multiple view can complete the work which a single view can not complete or complete difficultly.3D reconstruction based on multiple view is a hotspot research aspect in computer vision fields since it is inexpensive, convenient and simpler.
There are many key techniques of3D reconstruction based on multiple view such as feature detection and matching, fundamental matrix estimation, camera self-calibration,3D scene reconstruction, dense surface reconstruction and so on. The thesis is focused on fundamental matrix estimation, camera self-calibration and3D scene reconstruction. The main research work are as follows:
(1) Fundamental matrix estimation
In order to improve the accuracy of fundamental matrix estimation, an improved MLESAC algorithm is proposed. Firstly, according to the distances between the matching points and the corresponding epipolar lines, the superior correspondences are chosen, random sample consensus is adopted to sample the superior correspondences, and then the initial fundamental matrix is obtained using the Levenberg-Marquardt algorithm to minimize Sampson error; Secondly, according to the epipoplar geometry and adding constraints to detect matching points set, then the accuracy of matching points set is further improved; Finally, the accurate fundamental matrix is computed iteratively by using MLESAC. Experimental results show that the accuracy of our algorithm is improved, and the stability is good too.
(2) Camera self-calibration
In order to improve the accuracy of camera self-calibration. We propose a camera self-calibration method based on GA-PSO Algorithm. Firstly, the simplified Kruppa equations based on the SVD of the fundamental matrix is converted into the optimized cost function. Secondly, the minimum value of the optimized cost function is calculated by GA-PSO, then the intrinsic parameters of the camera are obtained. Because the GA-PSO algorithm combines with the advantages of genetic algorithm and particle swarm optimization, the accuracy of computation is improved. The experimental results show that the accuracy of camera self-calibration is improved.
(3)3D scene reconstruction
In view of the problems such as low accuracy of traditional reconstruction method by minimizing L∞norm, being sensitive to outliers and low efficiency,3D scene reconstruction based on L, and space point classification(marked as" L1-SPC") is presented. Firstly, L1approach is used to remove the outliers. Because we don't need to calculate the outliers in the following3D reconstruction, the accuracy of reconstruction can be improved and the computation time is reduced. Secondly, the scene points are divided into the two classes, the first class is the scene points which are visible in two-view; the second class is the scene points which are visible in more than two-view. The optimal triangulation method is used for the first class; The improved L∞-norm method is used for the second class. The gap between the upper and lower bound of bisection is kept as small as possible in the improved L∞-norm method, so the computation time is decreased. The experimental results show that the3D reconstruction based on L1-SPC method is higher accuracy and more efficient.
(4)3D scene plane reconstruction
In view of the problems such as low accuracy of3D scene plane reconstruction by using tranditional3D reconstruction method,3D scene plane reconstruction of two new model based on multiple view are presented. One is the model of scene plane reconstruction based on minimizing the reverse projection error, the other is the model of scene plane reconstruction based on minimizing the transfer error. The first model considering the knowledge that the reverse projection lines not only intersect but also meet in a scene plane, so minimizing the reverse projection error in the scene plane. The second model uses the transfer relation between the image plane and the scene plane, so minimizing the transfer error in the scene plane. At last, the optimized value is computed by GA. The basic principle of two methods is same, and the difference between them is the computational complexity. The experimental results show that the accuracy of two methods is almost same and the accurancy of3D scene plane reconstruction is improved greatly.
At the end of this dissertation, the main research is summarized. It makes out the main innovations and research achievements, and also points out the problems and issues which need to further research.
引文
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