Non-Minimal Sampling and Consensus for Prohibitively Large Datasets

2026-04-24

AI Summary

This work addresses the challenge of robust model estimation in large-scale, noisy data with outliers by proposing the NONSAC framework. Departing from conventional minimal sampling strategies, NONSAC repeatedly draws non-minimal subsets and employs robust estimators to generate multiple model hypotheses, which are then evaluated and selected via a unified scoring mechanism. This approach establishes an estimator-agnostic paradigm that significantly enhances scalability and robustness, and for the first time enables full-to-full point cloud registration without requiring point correspondences. The effectiveness of various scoring rules is validated across tasks including relative pose estimation, Perspective-n-Point, and point cloud registration, demonstrating the framework’s broad applicability and superior performance.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Non-convex Optimization
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

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.

Problem

• robust model estimation
• large-scale datasets
• outliers
• non-minimal sampling
• scalability

Innovation

• Non-minimal sampling
• Robust model estimation
• Scalable consensus
• Estimator-agnostic framework
• Correspondence-free registration