论文标题
广义可区分的兰萨克
Generalized Differentiable RANSAC
论文作者
论文摘要
我们提出了$ \ nabla $ -RANSAC，这是一种概括的可区分兰萨克，允许学习整个随机稳健估计管道。所提出的方法可以使用松弛技术来估计采样分布中的梯度，然后通过可区分的求解器传播。可训练的质量功能在$ \ nabla $ -RANSAC中估计的所有模型的分数中边缘化，以指导网络学习准确且有用的内置概率或训练功能检测和匹配网络。我们的方法直接最大程度地提出了良好假设的可能性，从而使我们能够学习更好的采样分布。我们在基本和基本矩阵估计的各种现实世界中测试$ \ nabla $ -RANSAC，以及3D点云注册，室外和室内，具有手工制作和基于学习的功能。在准确性方面，它以与其精确较差的替代方案相似的速度而优于最先进的。代码和训练有素的模型可在https://github.com/weitong8591/differentiable_ransac上找到。
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 distributions. We test $\nabla$-RANSAC on various real-world scenarios on fundamental and essential matrix estimation, and 3D point cloud registration, 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.
