Non-Minimal Sampling and Consensus for Prohibitively Large Datasets
For computer vision practitioners dealing with large-scale geometric estimation, NONSAC offers a scalable, estimator-agnostic framework that enhances robustness without requiring minimal samples.
NONSAC enables robust model estimation from arbitrarily large, noisy datasets by sampling non-minimal subsets and using a robust estimator, improving scalability and outlier robustness over RANSAC. It achieves strong results on camera pose estimation, PnP, and point cloud registration.
We introduce NONSAC (Non-Minimal Sampling and Consensus), a general framework for robust and scalable model estimation from arbitrarily large datasets contaminated with noise and outliers. NONSAC repeatedly samples non-minimal subsets of data and generates model hypotheses using a robust estimator, producing multiple candidate models. The final model is selected based on a predefined scoring rule that evaluates hypothesis quality. Our framework is estimator-agnostic and can be integrated with existing geometric fitting algorithms such as RANSAC to improve both scalability and robustness to outliers. We propose and evaluate various scoring rules for NONSAC on relative camera pose estimation, Perspective-n-Point, and point cloud registration. Furthermore, we showcase the applicability of NONSAC to correspondence-free point cloud registration by hypothesizing all-to-all correspondences.