Learning to Find Good Models in RANSAC

We propose the Model Quality Network, MQ-Net in short, for predicting the quality, e.g. the pose error of essential matrices, of models generated inside RANSAC. It replaces the traditionally used scoring techniques, e.g., inlier counting of RANSAC, truncated loss of MSAC, and the marginalization-based loss of MAGSAC++. Moreover, Minimal samples Filtering Network (MF-Net) is proposed for the early rejection of minimal samples that likely lead to degenerate models or to ones that are inconsistent with the scene geometry, e.g., due to the chirality constraint. We show on 54450 image pairs from public real-world datasets that the proposed MQ-Net leads to results superior to the state-of-the-art in terms of accuracy by a large margin. The proposed MF-Net accelerates the fundamental matrix estimation by five times and significantly reduces the essential matrix estimation time while slightly improving accuracy as well. Also, we show experimentally that consensus maximization, i.e. inlier counting, is not an inherently good measure of the model quality for relative pose estimation. The code is at https://github.com/danini/ learning-good-models-in-ransac. Linear (b x 1) Batch Normalization Leaky ReLU Sigmoid Linear (h x 1) Batch Normalization Leaky ReLU n blocks Model Error Model hypothesis h Error prediction from residuals by network f Residual histogram of h % points Point-to-model residual (px)