论文标题
强大的不确定性感知多视图三角剖分
Robust Uncertainty-Aware Multiview Triangulation
论文作者
论文摘要
我们提出了一种强大而有效的方法，用于多视图三角测量和不确定性估计。我们的贡献是三个方面：首先，我们使用带有中点方法的Twip-View RANSAC提出了一个异常拒绝方案。通过在三角剖分之前对两种视图样品进行预筛选，我们实现了最先进的效率。其次，我们比较了用于完善初始解决方案和嵌入式集合的不同局部优化方法。通过对Inlier集合的迭代更新，我们表明优化可显着提高准确性和鲁棒性。第三，我们将三角分点的不确定性建模为三个因素的函数：摄像机数量，平均再投影误差和最大视差角度。学习此模型使我们能够在测试时快速插入不确定性。我们通过广泛的评估来验证我们的方法。
We propose a robust and efficient method for multiview triangulation and uncertainty estimation. Our contribution is threefold: First, we propose an outlier rejection scheme using two-view RANSAC with the midpoint method. By prescreening the two-view samples prior to triangulation, we achieve the state-of-the-art efficiency. Second, we compare different local optimization methods for refining the initial solution and the inlier set. With an iterative update of the inlier set, we show that the optimization provides significant improvement in accuracy and robustness. Third, we model the uncertainty of a triangulated point as a function of three factors: the number of cameras, the mean reprojection error and the maximum parallax angle. Learning this model allows us to quickly interpolate the uncertainty at test time. We validate our method through an extensive evaluation.
