USAC: A Universal Framework for Random Sample Consensus

By Rahul Raguram, Ondřej Chum, Marc Pollefeys, Jiri Matas, Jan-Michael Frahm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013

676 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/c73a5a711ad8095e36bbab7fd13e3077b1398936

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Citations in the corpus

MAGSAC++, a Fast, Reliable and Accurate Robust Estimator (2019, methodology): “The two orthogonal improvements are combined with the ”bells and whistles” of USAC [21], e.g., pre-emptive verification, degeneracy testing.”
Understanding the Limitations of CNN-Based Absolute Camera Pose Regression (2019, methodology): “These 2D-3D matches are then used to estimate the camera pose by applying an npoint-pose (PnP) solver [2, 31–34] inside a RANSAC [18, 22, 36, 54] loop.”
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning (2020, background): “However, most research papers on this topic focus on estimating the Fundamental matrix [9, 10, 38, 3, 4], which is why we focus on this problem instead.”
Privacy Preserving Image-Based Localization (2019, methodology): “We used the same RANSAC threshold for all the methods, but in practice this threshold could be chosen smaller for the privacy preserving methods, since the point-to-line is always smaller than point-to-point reprojection errors.”
GMS: Grid-Based Motion Statistics for Fast, Ultra-Robust Feature Correspondence (2017, methodology): “SIFT [22], SURF [2], ORB [35], USAC [31] BF [16], BD [20], DM [44], GAIM [8], LIFT [47]”
MAGSAC: Marginalizing Sample Consensus (2018, methodology): “Also, as it was proposed for USAC [19], there are several ways of skipping early the evaluation of models which do not have the chance of being better than the previous so-farthe-best.”
Learning Two-View Correspondences and Geometry Using Order-Aware Network (2019, methodology): “USAC [28] provided a universal framework for RANSAC variants.”
Image Matching from Handcrafted to Deep Features: A Survey (2020, methodology): “Many improving strategies for RANSAC are integrated in USAC (Raguram et al. 2012).”
A review of multimodal image matching: Methods and applications (2021, neutral): “A universal framework that integrates many improving strategies of RANSAC is introduced in [310].”
Graph-Cut RANSAC (2017, methodology): “The idea of local optimization has been included in state-of-the-art RANSAC approaches like USAC [23].”
Learning to Find Good Correspondences (2017, background): “USAC [22] combines multiple advancements together into a unified framework.”