We propose $\nabla$-RANSAC, a generalized differentiable RANSAC that allows learning the entire randomized robust estimation pipeline. The proposed approach enables the use of relaxation techniques for estimating the gradients in the sampling distribution, which are then propagated through a differentiable solver. The trainable quality function marginalizes over the scores from all the models estimated within $\nabla$-RANSAC to guide the network learning accurate and useful inlier probabilities or to train feature detection and matching networks. Our method directly maximizes the probability of drawing a good hypothesis, allowing us to learn better sampling distribution. We test $\nabla$-RANSAC on a number of real-world scenarios on fundamental and essential matrix estimation, both outdoors and indoors, with handcrafted and learning-based features. It is superior to the state-of-the-art in terms of accuracy while running at a similar speed to its less accurate alternatives. The code and trained models are available at https://github.com/weitong8591/differentiable_ransac.

翻译：我们提出$\nabla$-RANSAC，一种通用可微RANSAC方法，能够学习整个随机鲁棒估计流程。该方案利用松弛技术估计采样分布中的梯度，并将其通过可微求解器进行传播。可训练的质量函数通过边缘化$\nabla$-RANSAC中所有模型估计的分数，引导网络学习准确且有用的内点概率，或训练特征检测与匹配网络。我们的方法直接最大化抽取优秀假设的概率，从而学习更优的采样分布。我们在基础矩阵和本质矩阵估计任务上，涵盖室外与室内场景，分别使用手工特征和基于学习的特征进行测试。该方法在保持与精度较低的替代方案相近运行速度的同时，在准确率上优于现有最先进技术。代码与预训练模型已开源：https://github.com/weitong8591/differentiable_ransac。