用确定性搜索替代随机采样，高效准确估计几何模型。
DS-SAC: Density Search for Sample Consensus
- 通过密度搜索替代随机采样，分步推进模型验证。
- 在大规模数据上实现更高AUC、更低误差和更快速度。
- 适合对效率与精度要求高的实际视觉任务场景。
鲁棒几何模型估计是计算机视觉中的基础问题。尽管RANSAC及其变体仍被广泛使用，但它们依赖于随机最小采样。本文提出密度搜索样本一致（DS-SAC）框架，一种避免重复随机采样的确定性鲁棒估计方法，通过搜索密集区域来提升效率。从初始模型出发，方法通过前向与后向搜索进行局部探索，并利用带符号残差递归划分点集，对每个有效分区搜索高一致性模型。实验表明，DS-SAC在点数上的复杂度为多项式，显著优于传统随机方法。在真实世界的大规模数据集上，针对单应性、基础矩阵和本质矩阵估计任务，其表现优于RANSAC、MAGSAC、LO-RANSAC和GC-RANSAC，在相同条件下取得更高的AUC值、可比或更低的中位姿态误差，且运行时间更短。
原文摘要 · Abstract (English)
Robust geometric model estimation is a fundamental problem in computer vision. RANSAC and its variants remain widely used for this task; however, they rely on stochastic minimal sampling. In this article, we propose Density Search Sample Consensus (DS-SAC), a deterministic robust estimation framework, that avoids repeated random sampling by searching dense regions. Starting from an initial model estimated from the available points, the method performs local exploration via forward and backward search. To facilitate global exploration, DS-SAC recursively partitions the point set using signed residuals and searches each valid partition for high-consensus models. We show that DS-SAC has polynomial complexity with respect to the number of points, making it an efficient alternative to stochastic consensus-based methods. Experiments on large-scale real-world datasets for homography, fundamental matrix, and essential matrix estimation show that DS-SAC achieves higher AUC scores, competitive or lower median pose errors, and faster runtime compared with widely used robust estimators, including RANSAC, MAGSAC, LO-RANSAC, and GC-RANSAC.
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