期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 41, 期 12, 页码 3022-3033出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2018.2871832
关键词
Cameras; Iterative methods; Linear programming; Time complexity; Robustness; Three-dimensional displays; Pose estimation; Perspective-< inline-formula xmlns:ali=http:; www; niso; org; schemas; ali; 1; 0; xmlns:mml=http:; www; w3; org; 1998; Math; MathML xmlns:xlink=http:; www; w3; org; 1999; xlink xmlns:xsi=http:; www; w3; org; 2001; XMLSchema-instance> < tex-math notation=LaTeX>$n$<; tex-math > < alternatives > < mml:math > < mml:mi > n <; mml:mi > <; mml:math > < inline-graphic xlink:href=zhou-ieq16-2871832; gif xlink:type=simple; > <; alternatives > <; inline-formula >-point; 1-point RANSAC; soft re-weighting; robustness to outliers
资金
- National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health [R01EB025964, P41EB015898, P41RR019703]
- Research Grant from Siemens-Healthineers USA
- National Institutes of Health through an R01 grant [R01EB025964]
The ability to handle outliers is essential for performing the perspective-$n$n-point (P$n$P) approach in practical applications, but conventional RANSAC+P3P or P4P methods have high time complexities. We propose a fast P$n$P solution named R1PP$n$P to handle outliers by utilizing a soft re-weighting mechanism and the 1-point RANSAC scheme. We first present a P$n$nP algorithm, which serves as the core of R1PP$n$nP, for solving the P$n$nP problem in outlier-free situations. The core algorithm is an optimal process minimizing an objective function conducted with a random control point. Then, to reduce the impact of outliers, we propose a reprojection error-based re-weighting method and integrate it into the core algorithm. Finally, we employ the 1-point RANSAC scheme to try different control points. Experiments with synthetic and real-world data demonstrate that R1PP$n$P is faster than RANSAC+P3P or P4P methods especially when the percentage of outliers is large, and is accurate. Besides, comparisons with outlier-free synthetic data show that R1PP$n$nP is among the most accurate and fast P$n$nP solutions, which usually serve as the final refinement step of RANSAC+P3P or P4P. Compared with REPP$n$nP, which is the state-of-the-art P$n$n. P algorithm with an explicit outliers-handling mechanism, R1PP$n$nP is slower but does not suffer from the percentage of outliers limitation as REPP$n$nP.
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