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

Finding Geometric Models by Clustering in the Consensus Space