从含噪数据中同时恢复多类几何模型，速度快且抗干扰强。
MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection
- 通过动态拟合与聚类融合的链接策略，自适应合并候选模型簇。
- 在多个公开数据集上优于主流方法，单类与多类任务均表现优异。
- 适合需要高鲁棒性建模的计算机视觉场景，如三维重建、图像匹配。
我们研究在噪声和离群点污染的数据集中，恢复多种不同类别几何结构的问题。具体而言，考虑由多种潜在参数模型（如平面与圆柱、单应矩阵与基础矩阵）构成的混合结构，通过偏好分析与聚类解决鲁棒拟合问题。本文提出一种新算法 MultiLink，可同时处理多类模型。该方法在新型链接机制中结合了实时模型拟合与模型选择，决定两个聚类是否合并。相比基于偏好分析的方法，MultiLink 具有诸多实用优势：速度更快、对内点阈值不敏感，并能弥补采样假设带来的局限。在多个公开数据集上的实验表明，MultiLink 在多类与单类问题中均显著优于现有先进方法。代码已公开下载。
原文摘要 · Abstract (English)
We address the problem of recovering multiple structures of different classes in a dataset contaminated by noise and outliers. In particular, we consider geometric structures defined by a mixture of underlying parametric models (e.g. planes and cylinders, homographies and fundamental matrices), and we tackle the robust fitting problem by preference analysis and clustering. We present a new algorithm, termed MultiLink, that simultaneously deals with multiple classes of models. MultiLink combines on-the-fly model fitting and model selection in a novel linkage scheme that determines whether two clusters are to be merged. The resulting method features many practical advantages with respect to methods based on preference analysis, being faster, less sensitive to the inlier threshold, and able to compensate limitations deriving from hypotheses sampling. Experiments on several public datasets demonstrate that Multi-Link favourably compares with state of the art alternatives, both in multi-class and single-class problems. Code is publicly made available for download.
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