Consensus-Adaptive RANSAC
Luca Cavalli Daniel Barath Marc Pollefeys Viktor Larsson
RANSAC and its variants are widely used for robust estimation, however, they
commonly follow a greedy approach to finding the highest scoring model while
ignoring other model hypotheses. In contrast, Iteratively Reweighted Least
Squares (IRLS) techniques gradually approach the model by iteratively updating
the weight of each correspondence based on the residuals from previous
iterations. Inspired by these methods, we propose a new RANSAC framework that
learns to explore the parameter space by considering the residuals seen so far
via a novel attention layer. The attention mechanism operates on a batch of
point-to-model residuals, and updates a per-point estimation state to take into
account the consensus found through a lightweight one-step transformer. This
rich state then guides the minimal sampling between iterations as well as the
model refinement. We evaluate the proposed approach on essential and
fundamental matrix estimation on a number of indoor and outdoor datasets. It
outperforms state-of-the-art estimators by a significant margin adding only a
small runtime overhead. Moreover, we demonstrate good generalization properties
of our trained model, indicating its effectiveness across different datasets
and tasks. The proposed attention mechanism and one-step transformer provide an
adaptive behavior that enhances the performance of RANSAC, making it a more
effective tool for robust estimation. Code is available at
https://github.com/cavalli1234/CA-RANSAC.