自动估算最优匹配阈值，提升RANSAC在无真值下的鲁棒性
Less Biased Noise Scale Estimation for Threshold-Robust RANSAC
- 通过修正模型拟合与噪声估计共用数据导致的偏差
- 提出多对点优化策略，使阈值估计更稳定可靠
- 无需真值数据，适合实际图像匹配场景使用
基于图像匹配的相对位姿估计标准方法是RANSAC。尽管RANSAC强大，但需手动设定内点阈值，以判断对应点误差是否足够小可纳入共识集。该阈值难以调优，尤其缺乏真值数据时。本文重新研究内点噪声尺度估计方法，发现现有方法存在偏差：一是模型拟合与噪声估计使用相同数据导致低估；二是未考虑阈值本身的影响。为此，本文修正了这些偏差，并提出基于SIMFIT++的多对点扩展方法，通过过滤估计值提升性能。实验表明，所提方法在多种阈值下均表现稳健。代码已开源。
原文摘要 · Abstract (English)
The gold-standard for robustly estimating relative pose through image matching is RANSAC. While RANSAC is powerful, it requires setting the inlier threshold that determines whether the error of a correspondence under an estimated model is sufficiently small to be included in its consensus set. Setting this threshold is typically done by hand, and is difficult to tune without an access to ground truth data. Thus, a method capable of automatically determining the optimal threshold would be desirable. In this paper we revisit inlier noise scale estimation, which is an attractive approach as the inlier noise scale is linear to the optimal threshold. We revisit the noise scale estimation method SIMFIT and find bias in the estimate of the noise scale. In particular, we fix underestimates from using the same data for fitting the model as estimating the inlier noise, and from not taking the threshold itself into account. Secondly, since the optimal threshold within a scene is approximately constant we propose a multi-pair extension of SIMFIT++, by filtering of estimates, which improves results. Our approach yields robust performance across a range of thresholds, shown in Figure 1. Code is available at https://github.com/Parskatt/simfitpp
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