基于未标定图像序列的三维重建

河北工业大学

硕士学位论文

基于未标定图像序列的三维重建

姓名：齐利超

申请学位级别：硕士

专业：计算机应用技术

指导教师：于明

20091101

河北工业大学硕士学位论文

基于未标定图像序列的三维重建

摘要

从二维图像获取三维世界信息一直是计算机视觉的主要研究目标。作为计算机视觉的一个重要分支，三维重建主要实现从二维图像计算出三维世界的模型。传统的三维重建是以摄像机标定为前提的，虽然可以获得较高的精度，但其应用范围受限。而基于未标定图像序列的三维重建，已引起越来越多的研究人员的关注，并提出多种重建方法。论文采用分层重建的思想，在完成射影重建的基础上，通过自标定获得摄像机内参提升模型到度量空间，得到了重建结果，涉及的关键技术有特征点检测与跟踪、基础矩阵的估计、三角化原理、集束调整、自标定和度量重建等。

首先，采用SIFT算子进行特征点的检测与跟踪，形成特征列表。相对于传统的SUSAN，Harris等算子，SIFT具有旋转、尺度缩放、亮度变化保持不变性，对视角变化、仿射变换、噪声也保持一定程度的稳定性。

其次，进行基于图像序列的射影重建。论文采用了RANSAC方法估计基础矩阵，关联图像序列，在此基础上，构建了射影投影矩阵。为准确求解空间点位置，提出了二阶迭代三角化方法，此方法具有适用于射影重建，计算误差小，重建的空间点精度高的优点。对于重建中误差的控制，采用集束调整，以获得更高精度的重建模型。射影重建时，局部集束调整与全局集束调整相结合。

最后，采用基于绝对二次曲线的分层自标定方法，获得了摄像机的内参，将重建模型从射影结构提升为度量结构，后集束调整，得到度量空间下的点云结构模型。

论文对真实图像序列进行了重建，验证了算法的可行性，且重建精度高，可满足影视、娱乐等一般化建模的需求。

关键词：特征点匹配与跟踪，基础矩阵，射影重建，自标定，三维重建，计算机视觉

基于未标定图像序列的三维重建

3D Reconstruction Based on Uncalibrated Image

Sequence

Abstract

It has always been a main target of computer vision to obtain our world’s 3D information from 2D images. As a main stream of computer vision, 3D reconstruction tries to get 3D world model from images. Traditional 3D reconstruction can rebuild a more precise model, on the condition of the camera being calibrated, which limits its application. Many researchers focus on 3D reconstruction based on uncalibrated images, and have proposed many methods. With Stratified methods, our project upgrade projective model to metric one with the intrinsic parameters computed from self-calibration phase. The involved key technologies include: feature detection and tracking, computation of fundamental matrix, triangulation, bundle adjustment, self-calibration, and metric reconstruction.

First of all, Features are detected and tracked with SIFT, forming feature table. Comparing with most feasible feature detector such as SUSAN, Harris, SIFT is invariant of image scale, rotation, change of illumination, roust to change in 3D viewpoint, affine distortion and addition of noise.

Secondly, 3D projective model is rebuilt based on image sequence. Fundamental matrix is computed with RANSAC method, relating the image sequence, with which projective matrix is computed. A second order iterative triangulation method is proposed to allocate points in 3D space. This method can be used in projective reconstruction, having little computation and higher precision. Bundle adjustment is adopted to control error and get a more precise model. We use local adjustment and global one in projective reconstruction.

At last, Stratified self-calibration based on absolute conic is adopted to compute the camera intrinsic parameters. The metric point cloud model which is updated from projective one with camera parameters is adjusted with bundle adjustment.

A precise model is reconstructed from image sequence, demonstrating our method’s feasibility. And the precision can satisfy the need of movie and entertainment etc.

KEY WORDS: feature extraction and matching, fundamental matrix, projective reconstruction, self-calibration, 3D reconstruction, computer vision