A new RANSAC score obtained by marginalizing inlier scale in closed form under an Inverse-Gamma prior, outperforming threshold-based baselines on a 70k-pair benchmark while remaining insensitive to scale miscalibration.
Progressive NAPSAC: sampling from gradually growing neighborhoods
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We propose Progressive NAPSAC, P-NAPSAC in short, which merges the advantages of local and global sampling by drawing samples from gradually growing neighborhoods. Exploiting the fact that nearby points are more likely to originate from the same geometric model, P-NAPSAC finds local structures earlier than global samplers. We show that the progressive spatial sampling in P-NAPSAC can be integrated with PROSAC sampling, which is applied to the first, location-defining, point. P-NAPSAC is embedded in USAC, a state-of-the-art robust estimation pipeline, which we further improve by implementing its local optimization as in Graph-Cut RANSAC. We call the resulting estimator USAC*. The method is tested on homography and fundamental matrix fitting on a total of 10,691 models from seven publicly available datasets. USAC* with P-NAPSAC outperforms reference methods in terms of speed on all problems.
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Proposes three efficient minimal solvers for relative pose estimation in multi-camera autonomous driving systems by reducing point correspondences via new parameterization and motion priors.
Two new minimal solvers for multi-camera visual-inertial relative pose that use four points and IMU direction priors to reach a univariate 6th-degree polynomial.
citing papers explorer
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RANSAC Scoring Done Right
A new RANSAC score obtained by marginalizing inlier scale in closed form under an Inverse-Gamma prior, outperforming threshold-based baselines on a 70k-pair benchmark while remaining insensitive to scale miscalibration.
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Efficient Minimal Solvers for Relative Pose Estimation in Autonomous Driving Applications
Proposes three efficient minimal solvers for relative pose estimation in multi-camera autonomous driving systems by reducing point correspondences via new parameterization and motion priors.
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Efficient Minimal Solvers for Visual-Inertial Relative Pose Estimation in Multi-Camera Systems
Two new minimal solvers for multi-camera visual-inertial relative pose that use four points and IMU direction priors to reach a univariate 6th-degree polynomial.