%0 Journal Article
%J IEEE Trans Pattern Anal Mach Intell
%D 2019
%T Re-weighting and 1-Point RANSAC-Based P nP Solution to Handle Outliers
%A Zhou, Haoyin
%A Zhang, Tao
%A Jagadeesan, Jayender
%X The ability to handle outliers is essential for performing the perspective- n-point (P nP) approach in practical applications, but conventional RANSAC+P3P or P4P methods have high time complexities. We propose a fast P nP solution named R1PP nP to handle outliers by utilizing a soft re-weighting mechanism and the 1-point RANSAC scheme. We first present a P nP algorithm, which serves as the core of R1PP nP, for solving the P 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 nP 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 nP is among the most accurate and fast P nP solutions, which usually serve as the final refinement step of RANSAC+P3P or P4P. Compared with REPP nP, which is the state-of-the-art P nP algorithm with an explicit outliers-handling mechanism, R1PP nP is slower but does not suffer from the percentage of outliers limitation as REPP nP.
%B IEEE Trans Pattern Anal Mach Intell
%V 41
%P 3022-33
%8 2019 Dec
%G eng
%N 12
%1 http://www.ncbi.nlm.nih.gov/pubmed/31689179?dopt=Abstract
%R 10.1109/TPAMI.2018.2871832