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Triangulation for Points on Lines

机译:线上的点的三角剖分

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摘要

Triangulation consists in finding a 3D point reprojecting the best as possible onto corresponding image points. It is classical to minimize the reprojection error, which, in the pinhole camera model case, is nonlinear in the 3D point coordinates. We study the triangulation of points lying on a 3D line, which is a typical problem for Structure-From-Motion in man-made environments. We show that the reprojection error can be minimized by finding the real roots of a polynomial in a single variable, which degree depends on the number of images. We use a set of transformations in 3D and in the images to make the degree of this polynomial as low as possible, and derive a practical reconstruction algorithm. Experimental comparisons with an algebraic approximation algorithm and minimization of the reprojection error using Gauss-Newton are reported for simulated and real data. Our algorithm finds the optimal solution with high accuracy in all cases, showing that the polynomial equation is very stable. It only computes the roots corresponding to feasible points, and can thus deal with a very large number of views - triangulation from hundreds of views is performed in a few seconds. Reconstruction accuracy is shown to be greatly improved compared to standard triangulation methods that do not take the line constraint into account.
机译:三角剖分在于找到3D点,将3D点尽可能最佳地重新投影到相应的图像点上。最小化重投影误差是经典做法,在针孔相机模型的情况下,该误差在3D点坐标中是非线性的。我们研究了3D线上的点的三角剖分,这是人造环境中“从结构运动”的典型问题。我们表明,可以通过在单个变量中找到多项式的实根来最小化重新投影误差,该阶数取决于图像的数量。我们在3D和图像中使用了一组转换,以使该多项式的阶数尽可能低,并推导了一种实用的重建算法。对于模拟和真实数据,报告了使用代数近似算法进行的实验比较以及使用高斯-牛顿算法将重投影误差最小化的情况。我们的算法在所有情况下都能找到高精度的最优解,这表明多项式方程非常稳定。它仅计算对应于可行点的根,因此可以处理大量视图-在几秒钟内执行数百个视图的三角剖分。与不考虑线约束的标准三角剖分方法相比,重构精度显示出了极大的提高。

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