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快速鲁棒的大场景三维重建
崔海楠
学位类型工学博士
导师胡占义
2016-05
学位授予单位中国科学院大学
学位授予地点北京
关键词从运动恢复结构 先验信息融合 三维建模 广义摄像机模型 匹配点轨迹选取
摘要随着图像传感器技术的快速发展,大量高分辨率的图像对基于图像的快速
大场景三维重建算法提出了巨大的挑战。本文主要针对大场景三维重建中速度
和精度的两个问题进行系统研究,提出多种快速鲁棒的全局式大场景三维重建
算法,主要工作包括以下几方面:
1. 针对有先验信息的图像重建,提出了一种融合有噪先验信息的大场景
三维重建方法。首先利用图像拍摄时的先验信息,比如GPS信息、IMU信息以
及指南针信息等,初始化摄像机的拍摄位姿。然后,根据初始的摄像机旋转矩
阵,迭代地判别外极几何图中边的潜在内点,并利用非线性最小二乘方法优化
摄像机旋转矩阵。在此基础上,根据初始的摄像机位置,迭代地发现匹配点轨
迹中的潜在内点,通过反复捆绑调整优化摄像机的位置和空间三维点云。大量
不同类型图像重建实验表明,我们的方法与很多优秀的重建方法在重建精度和
场景结构上相当,但是在大场景重建速度和鲁棒性上我们表现更好。
2. 针对多摄像机拍摄系统,提出一种全局式融合广义摄像机模型的大场景
三维重建方法。在多摄像机系统拍摄图像的过程中,多个摄像机内部的相对旋
转矩阵和相对平移关系是保持不变的。我们将此先验信息融入到摄像机位姿参
数优化中,通过引入广义摄像机模型,使得最终捆绑调整得到的摄像机位姿更
加符合真实拍摄的物理模型。通过对街景车和无人机倾斜射影两种多摄像机拍
摄系统进行真实图像测试,该方法不仅提高了三维重建系统的速度,而且重建
得到的场景结构和精度也得到一定的提升。
3. 为了提高大场景三维重建中捆绑调整的速度,提出一种基于外极几何图
覆盖来进行匹配点轨迹选取的方法。首先,我们根据匹配点轨迹的长度、尺度
和平均重投影误差三项准则,对匹配点轨迹的可信度进行排序。然后,通过覆
盖外极几何图的生成树以保证摄像机之间的连接关系。最后,随着多次外极几
何图生成树的构建,得到一组匹配点轨迹的子集用以进行后续的迭代三角化和
捆绑调整。大量图像重建实验表明,重建系统在融合该方法后,不仅提升了三
维重建系统的速度,而且极大地降低了大场景三维重建时所需要的内存消耗,
使得重建系统可拓展性更强。
4. 针对没有先验GPS信息或者仅有部分GPS信息的图像重建问题,提出一
种全局式的摄像机位姿求取方法。首先,利用外极几何图上相对旋转与绝对
旋转矩阵的关系,求取摄像机的绝对旋转矩阵。然后,基于RANSAC技术,利
用2点法重新计算外极几何边上的相对平移方向,并根据优化后的相对平移方
向和匹配点轨迹计算外极几何边模长的比值。最后,利用该比值和相对平移方
向,通过凸的L1问题优化求解摄像机的绝对位置。通过在网络抓取图像和无人
机图像数据上的测试,该方法与很多优秀的重建方法在重建精度和场景结构上
相当,但是在有部分GPS信息时,它可以内在地融合这些信息进行求解。

其他摘要With the development of camera sensor technology, a large number of highresolution
images bring a big challenge to the large-scale 3D reconstruction. This
thesis is focused on both the reconstruction accuracy and computation efficiency.
To this end, we propose a series of fast and accurate large-scale 3D reconstruction
algorithms. Our main contributions include:
1. For the images with auxiliary imaging information, an efficient, accurate
and scalable reconstruction method is proposed by fusing these noisy prior
information. Firstly,initial camera poses are obtained by the auxiliary imaging
information, including GPS、IMU and compass angles. Then, based on the
initial camera rotations, potential epipolar geometry edge inliers are iteratively
distinguished and used for optimizing global camera rotations by non-linear least
square algorithm. Given the global camera rotations and initial camera locations,
potential tracks inliers are iteratively found and used for optimizingThe camera poses and scene structure by bundle adjustment. Extensive experimental
results show that our method performs similarly or better than many of the
state-of-art reconstruction approaches, in terms of reconstruction accuracy and
completeness, but is more efficient and scalable for large-scale image datasets.
2. For the multi-camera system, a large-scale scene reconstruction method
is proposed by fusing generalized camera model in a global manner. Since the
relative rotations and translations in the multi-camera system are fixed during
the image capturing process, we fuse this prior model information into the optimization
algorithm to inherently make the final camera poses satisfy the real
image capturing model. According to the experimental reconstruction results
on both street view system and oblique airborne photogrammetry system, our
method not only accelerates the speed of reconstruction, but improves the scene
structure and reconstruction accuracy in some degree.
3. To improve the computation efficiency of bundle adjustment, we propose
an efficient tracks selection method by covering the epipolar geometry graph.
Firstly, according to three selection rules: Redundancy, Accuracy and Connectivity,
the tracks priority are ranked by their lengths, scales and costs. Then, the
tracks selection problem is solved by covering the spanning tree of the epipolar
geometry graph. Finally, with the coverage of multiple spanning trees, a compact
set of tracks is obtained for the final bundle adjustment. Extensive experiments
show that the new reconstruction system with our tracks selection module performs
similarly or better than many of the state-of-art approaches in terms of
reconstruction completeness, but is more efficient and scalable for large-scale
image datasets.
4. For the images without or with a part of GPS information, we propose
a robust global camera poses estimation method. Firstly,by minimizing
the differences between relative rotations and absolute rotations on the epipolar
geometry edges, the whole absolute camera rotations are obtained. Then,
with RANSAC technique, the relative translations are re-estimated by a 2-point
algorithm. With refined translations and tracks, the scale-ratio between two
translations is computed by constructing adjacent triangles. Finally, with the
scale-ratio estimations and refined translations, the camera centers estimation
problem is tackled by solving convex L1 optimization problems. According to
the experimental reconstruction results on both Internet images and unmanned
aerial vehicle images, our method performs similarly or better than many of the
state-of-art reconstruction approaches, but it could inherently fuse the auxiliary
GPS information.
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/11719
专题毕业生_博士学位论文
作者单位中国科学院自动化所
推荐引用方式
GB/T 7714
崔海楠. 快速鲁棒的大场景三维重建[D]. 北京. 中国科学院大学,2016.
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