Adaptive Reordering Sampler with Neurally Guided MAGSAC

• URL: http://arxiv.org/abs/2111.14093v3
• Date: Fri, 8 Sep 2023 16:23:49 GMT
• Title: Adaptive Reordering Sampler with Neurally Guided MAGSAC
• Authors: Tong Wei, Jiri Matas, Daniel Barath
• Abstract summary: 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. 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.
• Score: 63.139445467355934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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