ARSAC: Efficient model estimation via adaptively ranked sample consensus

作者：Li, Rui; Sun, Jinqiu^*; Gong, Dong; Zhu, Yu; Li, Haisen; Zhang, Yanning

来源：Neurocomputing, 2019, 328: 88-96.

DOI：10.1016/j.neucom.2018.02.103

• RANSAC
• Robust model estimation
• Efficiency
• Adaptively ranked measurements
• Non-uniform sampling
• Geometric constraint

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.

• 出版日期2019-2-7
• 单位西北工业大学