Adaptive Reordering Sampler with Neurally Guided MAGSAC

TL;DR

This paper introduces ARS-MAGSAC, a novel robust sampling method guided by neural inlier probabilities and a new loss function, improving accuracy and efficiency in two-view geometry estimation tasks.

Contribution

It presents a new adaptive sampler with neural guidance and a geometrically justified loss for better scene geometry estimation, outperforming existing methods.

Findings

Outperforms state-of-the-art in accuracy and speed

Effective on PhotoTourism and KITTI datasets

Improves two-view geometry estimation

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.