Efficient sampling using feature matching and variable minimal structure size

Abstract

Greedy search-based guided sampling is a promising research field in model fitting to data with multiple structures in the presence of a large number of outliers. However, these greedy search-based guided sampling algorithms are sensitive to the fixed minimal (acceptable) structure size and the initial model hypothesis: when the fixed minimal structure size is too small, data subsets sampled by these algorithms are not representative. In contrast, when it is too large, data subsets might be contaminated by outliers. Furthermore, these algorithms may fail to obtain an accurate model hypothesis, if the initial model hypothesis is far from the true model. In this paper, we address the above-mentioned two issues by proposing two greedy search-based strategies: one aims to adaptively estimate minimal structure sizes and the other aims to generate effective initial model hypotheses. Specifically, on one hand, to avoid using the fixed minimal structure size, a strategy is proposed to adaptively estimate minimal structure sizes by using previously obtained ones. On the other hand, to reduce the impact of outliers, a strategy is proposed to filter out outliers to obtain a reduced data subset by using a feature matching algorithm. Then, this strategy generates promising initial model hypotheses by using a proximity sampling on the reduced data subset. Finally, an efficient sampling algorithm based on the two proposed greedy search-based strategies is applied to three vision tasks, i.e., fundamental matrix estimation, homography plane detection and 3D motion segmentation. Extensive experimental results demonstrate the effectiveness of the proposed sampling algorithm.