We propose a new sampler for robust estimators that always selects the sample with the highest probability of consisting only of inliers. After every unsuccessful iteration, the inlier probabilities are updated in a principled way via a Bayesian approach. The probabilities obtained by the deep network are used as prior (so-called neural guidance) inside the sampler. Moreover, we introduce a new loss that exploits, in a geometrically justifiable manner, the orientation and scale that can be estimated for any type of feature, e.g., SIFT or SuperPoint, to estimate two-view geometry. The new loss helps to learn higher-order information about the underlying scene geometry. Benefiting from the new sampler and the proposed loss, we combine the neural guidance with the state-of-the-art MAGSAC++. Adaptive Reordering Sampler with Neurally Guided MAGSAC (ARS-MAGSAC) is superior to the state-of-the-art in terms of accuracy and run-time on the PhotoTourism and KITTI datasets for essential and fundamental matrix estimation. The code and trained models are available at https://github.com/weitong8591/ars_magsac.

翻译：我们提出了一种用于鲁棒估计器的新采样器，该采样器始终选择由内点构成概率最高的样本。每经过一次不成功的迭代，内点概率会通过贝叶斯方法以原则性的方式进行更新。由深度网络获得的概率在采样器内部被用作先验（即所谓的神经引导）。此外，我们引入了一种新的损失函数，该函数以几何上合理的方式利用可从任何类型特征（例如SIFT或SuperPoint）估计的方向和尺度来估计两视图几何。新损失有助于学习关于底层场景几何的高阶信息。得益于新采样器和所提出的损失函数，我们将神经引导与最先进的MAGSAC++相结合。自适应重排序采样器与神经引导的MAGSAC（ARS-MAGSAC）在PhotoTourism和KITTI数据集上用于本质矩阵和基础矩阵估计时，在精度和运行时间方面均优于现有最先进方法。代码和训练模型可在https://github.com/weitong8591/ars_magsac获取。