提出新框架NONSAC，高效处理超大数据集中的噪声与异常值。
Non-Minimal Sampling and Consensus for Prohibitively Large Datasets
- 通过反复采样非最小数据子集生成候选模型，提升鲁棒性。
- 在相机位姿估计等任务中显著提升准确率与可扩展性。
- 无需依赖特定算法，适合大规模点云等场景应用。
我们提出NONSAC（非最小采样与共识）框架，用于从任意大规模、含噪声和异常值的数据集中进行稳健且可扩展的模型估计。NONSAC反复采样非最小数据子集，并利用鲁棒估计器生成模型假设，产生多个候选模型；最终模型根据预定义评分规则选择，评估假设质量。该框架对估计器无依赖性，可集成现有几何拟合算法（如RANSAC），提升可扩展性与抗异常值能力。我们在相对相机位姿估计、透视n点问题（PnP）及点云配准任务中评估了多种评分规则。此外，通过假设全对全对应关系，展示了NONSAC在无对应点云配准中的适用性。
原文摘要 · Abstract (English)
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.
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