Efficient guided hypothesis generation for multi-structure epipolar geometry estimation

•A sampling method EGHG is proposed for multi-structure geometry estimation.•EGHG combines the benefits of a global and a local sampling strategy.•The global sampling strategy is designed to rapidly obtain promising solutions.•The local sampling strategy is designed to efficiently achieve accurate solutions.•Experimental results show the effectiveness of EGHG on public real image pairs.

We propose an Efficient Guided Hypothesis Generation (EGHG) method for multi-structure epipolar geometry estimation. Based on the Markov Chain Monte Carlo process, EGHG combines two guided sampling strategies: a global sampling strategy and a local sampling strategy. The global sampling strategy, guided by using both spatial sampling probabilities and keypoint matching scores, rapidly obtains promising solutions. The spatial sampling probabilities are computed by using a normalized exponential loss function. The local sampling strategy, guided by using both Joint Feature Distributions (JFDs) and keypoint matching scores, efficiently achieves accurate solutions. In the local sampling strategy, EGHG updates a set of current best hypothesis candidates on the fly, and then computes JFDs between the input data and these candidates. Experimental results on public real image pairs show that EGHG significantly outperforms several state-of-the-art sampling methods on multi-structure data.