基于仿射近似从序列图像重建目标三维结构

从序列图像重建目标三维结构是摄影测量学和计算机视觉的研究热点。针对传统算法中精度与计算量的矛盾,提出一种基于仿射近似的新算法,首先用仿射三线性张量合成序列图像中的缺失图像点,重新建立图像测量矩阵,然后用仿射矩阵分解从图像特征直接获得目标三维结构的初始值,最后用基于“R-T”模式的光束法平差进行优化。仿真数据和真实图像实验表明,新算法精度高、稳定性好,有效解决了传统算法中精度、算法复杂性与结果数值稳定性的矛盾,为序列图像三维解析中的关键技术问题提供了解决方案。算法可广泛应用于航空测绘、工业检测、虚拟现实、动画制作以及军事侦察等领域。

Three-Dimensional Structure Reconstruction from Image Sequence Based on Affine Approximation

Three-dimensional structure reconstruction from image sequence is acquiring much attention in photogrammetry and computer vision.A new algorithm based on affine approximation is put forward to resolve the contradiction of accuracy and robustness in conventional algorithms.Firstly,it synthesizes those missing points in image sequence based on tri-linear tensor,and then rebuilds a new measurement matrix without missing elements,which is decomposed by the bilinear affine factorization method to recover the initial data for the three-dimensional structure of objects.Finally,the initial threedimensional reconstruction is optimized by the "R-T"-model-based bundle adjustment.Synthetic and real image tests show the new method presents more accurate and robust result and it can be applied to aerial surveying and mapping,industry inspection,virtual reality,cartoon making,military reconnaissance,etc..