ARSAC: Efficient model estimation via adaptively ranked sample consensus

doi:10.1016/j.neucom.2018.02.103

ARSAC: Efficient model estimation via adaptively ranked sample consensus

2019-02-01

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DOI：10.1016/j.neucom.2018.02.103

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RANSAC is a popular robust model estimation algorithm in various computer vision applications. However, the speed of RANSAC declines dramatically as the inlier rate of the measurements decreases. In this paper, a novel Adaptively Ranked Sample Consensus(ARSAC) algorithm is presented to boost the speed and robustness of RANSAC. The algorithm adopts non-uniform sampling based on the ranked measurements to speed up the sampling process. Instead of a fixed measurement ranking, we design an adaptive scheme which updates the ranking of the measurements, to incorporate high quality measurements into sample at high priority. At the same time, a geometric constraint is proposed during sampling process to select measurements with scattered distribution in images, which could alleviate degenerate cases in epipolar geometry estimation. Experiments on both synthetic and real-world data demonstrate the superiority in efficiency and robustness of the proposed algorithm compared to the state-of-the-art methods. (c) 2018 Elsevier B.V. All rights reserved.

Keyword:

RANSAC

Robust model estimation

Efficiency

Adaptively ranked measurements

Non-uniform sampling

Geometric constraint

ARSAC: Efficient model estimation via adaptively ranked sample consensus

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