一种鲁棒性的射影重建方法——加权迭代法

A Robust Projective Reconstruction Method Based on Iterative Weight

本文提出了一种利用加权迭代来剔除出格数据并实现射影重建的方法,该方法首先利用加权来进行射影重建,再利用重投影误差的倒数作为下一次迭代的权值,如此循环,就可以使出格数据的权值接近于0,最后完成射影重建。本方法可以克服最小二乘法鲁棒性差及随机抽样算法计算量大的缺点,模拟实验和真实实验数据结果表明,该重建方法具有运算量小、鲁棒性好等优点。

The paper presents a robust projective reconstruction method based on iterative weight, which can efficiently discard the outliers. The weight of each point is determined based on the inverse of re-projective error and the projective reconstruction is obtained based on the weight. After several iterations, the weights of the outliers trend to zero and the projective reconstruction is obtained with good accuracy. The method can overcome the disadvantages of both the least-squares method and the Random Sample and Consesus method. The theory and experiments with both simulate and real data demonstrate that the method is very efficient and robust.