CAJ | 학술논문

在射影重建问题中,广泛应用的分解算法尽管能够平等地看待所有的图像,但它们要求全部空间点在所有的图像上都可视.为了克服这一限制,该文提出了一种基于分解算法的射影重建方法.该方法迭代地估计空间点、投影矩阵、深度因子和丢失的数据.投影矩阵和空间点通过奇异值分解被求取.深度因子则根据每幅所有图像点构成的行向量生成的线性子空间和与射影重建空间点构成的行向量生成的线性子空间是同一线性子空间的特性被估计出来.模拟数据和真实图像的实验结果表明,该射影重建方法具有重投影误差小,收敛性好和实用性强的特点.
재사영중건문제중,엄범응용적분해산법진관능구평등지간대소유적도상,단타문요구전부공간점재소유적도상상도가시.위료극복저일한제,해문제출료일충기우분해산법적사영중건방법.해방법질대지고계공간점、투영구진、심도인자화주실적수거.투영구진화공간점통과기이치분해피구취.심도인자칙근거매폭소유도상점구성적행향량생성적선성자공간화여사영중건공간점구성적행향량생성적선성자공간시동일선성자공간적특성피고계출래.모의수거화진실도상적실험결과표명,해사영중건방법구유중투영오차소,수렴성호화실용성강적특점.
Factorization algorithms are widely used in projective reconstruction, which has an inherent advantage of being able to handle any number of images simultaneously without special treatment for any subgroup of views. However, these algorithms assume that all object points are visible on all images. In order to overcome the limitation, we present an algorithm to estimate projective shape, projection matrices, projective depths and missing data iteratively. Estimation problems of projective shape and projection matrices are solved in terms of singular value decomposition. According to the fact that the sum of linear subspaces, each of which is spanned by the rows of all points in each image, is equal to the linear subspace spanned by the rows of space points, projective depths are estimated. Experimental results with both synthetic data and real images show that the proposed method has small reprojection errors, good convergence property and practicality.