Random Sample Consensus: A Paradigm for Model Fitting with Apphcatlons to Image Analysis and Automated Cartography
在实现人脸姿态估计时，通过opencv自带的DLT（solvepnp）解算出来的值误差很大，而且不稳定。
经过一系列思考和验证，发现实现上并无差错。那么是什么造成了呢？
这是二阶段或多阶段模型的共性错误：通常为分类误差引起的gross error。按照论文所述，分类误差是不服从于正态分布的，所以难以平滑。但可以使用随机采样一致性解决。
随机采样一致性的大致步骤，可以用伪代码表示：
Given:
data – a set of observed data points
model – a model that can be fitted to data points
n – the minimum number of data values required to fit the model
k – the maximum number of iterations allowed in the algorithm
t – a threshold value for determining when a data point fits a model
d – the number of close data values required to assert that a model fits well to data
Return:
bestfit – model parameters which best fit the data (or nul if no good model is found)
iterations = 0
bestfit = nul
besterr = something really large
while iterations < k {
maybeinliers = n randomly selected values from data
maybemodel = model parameters fitted to maybeinliers
alsoinliers = empty set
for every point in data not in maybeinliers {
if point fits maybemodel with an error smaller than t
add point to alsoinliers
}
if the number of elements in alsoinliers is > d {
% this implies that we may have found a good model
% now test how good it is
bettermodel = model parameters fitted to all points in maybeinliers and alsoinliers
thiserr = a measure of how well model fits these points
if thiserr < besterr {
bestfit = bettermodel
besterr = thiserr
}
}
increment iterations
}
return bestfit
论文简单讨论了随机采样一致性的k、t值，t值应该取测量平均误差的两到三倍。
而k值，则给出了公式：
z表示的是可信度，b则表示随机选取一次，得到合适模型的概率。
当真实值淹没在噪音中，也可以使用该方法来找出群内点，但是如果b值过低，该方法是无效的。