具有不确定模型的点、线段和平面的统一分解算法

Daniel Morris, T. Kanade
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引用次数: 138

摘要

本文提出了一种利用点特征、线段和平面从图像序列中恢复结构和运动的统一分解算法。该公式基于特征的方向性不确定性模型。点和线段都由相同的概率模型描述,因此可以用相同的方法恢复。关于特征共平面性的先验信息被证明可以自然地拟合到新的分解公式中,并为形状恢复提供了额外的约束。该公式导致加权最小二乘运动和形状恢复问题,该问题由有效的准线性算法解决。统计不确定性模型还使我们能够恢复重建的三维特征位置的不确定性估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A unified factorization algorithm for points, line segments and planes with uncertainty models
In this paper we present a unified factorization algorithm for recovering structure and motion from image sequences by using point features, line segments and planes. This new formulation is based on directional uncertainty model for features. Points and line segments are both described by the same probabilistic models and so can be recovered in the same way. Prior information on the coplanarity of features is shown to fit naturally into the new factorization formulation and provides additional constraints for the shape recovery. This formulation leads to a weighted least squares motion and shape recovery problem which is solved by an efficient quasi-linear algorithm. The statistical uncertainty model also enables us to recover uncertainty estimates for the reconstructed three dimensional feature locations.
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