We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. The problem is formalized as finding dominant model instances progressively without forming crisp point-to-model assignments. Dominant instances are found via a RANSAC-like sampling and a consolidation process driven by a model quality function considering previously proposed instances. New ones are found by clustering in the consensus space. This new formulation leads to a simple iterative algorithm with state-of-the-art accuracy while running in real-time on a number of vision problems - at least two orders of magnitude faster than the competitors on two-view motion estimation. Also, we propose a deterministic sampler reflecting the fact that real-world data tend to form spatially coherent structures. The sampler returns connected components in a progressively densified neighborhood-graph. We present a number of applications where the use of multiple geometric models improves accuracy. These include pose estimation from multiple generalized homographies; trajectory estimation of fast-moving objects; and we also propose a way of using multiple homographies in global SfM algorithms. Source code: https://github.com/danini/clustering-in-consensus-space.

翻译：我们提出了一种新算法，用于寻找未知数量的几何模型（例如单应性矩阵）。该问题被形式化为逐步发现主导模型实例，而无需形成清晰的点对模型分配。通过类似RANSAC的采样和由考虑先前提出实例的模型质量函数驱动的整合过程，发现主导实例。新实例通过在共识空间中聚类获得。这一新公式产生了一种简单的迭代算法，在众多视觉问题上实现了实时运行且达到最先进精度——在双视图运动估计中，速度比竞品快至少两个数量级。此外，我们提出了一种确定性采样器，反映了现实世界数据倾向于形成空间连贯结构的特点。该采样器在逐步稠密的邻域图中返回连通分量。我们展示了若干应用，其中使用多个几何模型提高了精度。这些应用包括：基于多个广义单应性矩阵的位姿估计、快速移动物体的轨迹估计，以及一种在全局SfM算法中使用多个单应性矩阵的方法。源代码：https://github.com/danini/clustering-in-consensus-space。