平移平均中的基线脱敏
Baseline Desensitizing In Translation Averaging
摘要：
许多现有的平移平均算法对不同的相机基线敏感，并且必须依赖于广泛的预处理来改善观察到的对极几何图形，或者如果它们对不同的相机基线是鲁棒的，则需要复杂的优化来最小化高度非线性的角度误差目标。在本文中，我们精心设计了一个简单而有效的双线性目标函数，引入一个变量来执行必要的归一化。目标函数具有角度误差的基线不敏感特性，但可以通过块坐标下降进行简单有效的优化，具有良好的经验性能。进一步提出了一种旋转辅助的迭代加权最小二乘方法来处理异常值。我们还有助于更好地理解最近的两个凸算法，LUD和Shapefit/kick的行为，澄清潜在的微妙差异，导致性能差距。最后，我们证明了我们的算法在基准数据集中实现了整体优越的精度相比，国家的最先进的方法，也是几倍的速度。
英文摘要：
Many existing translation averaging algorithms are either sensitive to disparate camera baselines and have to rely on extensive preprocessing to improve the observed Epipolar Geometry graph, or if they are robust against disparate camera baselines, require complicated optimization to minimize the highly nonlinear angular error objective. In this paper, we carefully design a simple yet effective bilinear objective function, introducing a variable to perform the requisite normalization. The objective function enjoys the baseline-insensitive property of the angular error and yet is amenable to simple and efficient optimization by block coordinate descent, with good empirical performance. A rotation-assisted Iterative Reweighted Least Squares scheme is further put forth to help deal with outliers. We also contribute towards a better understanding of the behavior of two recent convex algorithms, LUD and Shapefit/kick, clarifying the underlying subtle difference that leads to the performance gap. Finally, we demonstrate that our algorithm achieves overall superior accuracies in benchmark dataset compared to state-of-theart methods, and is also several times faster.
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