Robust trifocal tensor constraints for structure from motion estimation

It is important to estimate accurate camera parameters in multi-view stereo. In this paper, we use three-view relations, the trifocal tensor, to improve the Bundler, a popular structure from motion (SfM) system, for estimating accurate camera parameters. We propose a novel method: the Robust Orthogonal Particle Swarm Optimization (ROPSO) to estimate a robust and accurate trifocal tensor. In ROPSO, we formulate the trifocal tensor estimation as a global optimization problem and use the particle swarm optimization (PSO) for parameter searching. The orthogonal array is used to select the representative initial particles in PSO for more stable results. In the experiments, we use simulated and real ground truth data for statistical analysis. The experimental results show that the proposed ROPSO can achieve more accurate estimation of the trifocal tensor than the traditional methods and has higher probability to find the optimization solution than the traditional methods. Based on the trifocal tensor estimated by the proposed method, the SfM estimation errors can effectively be reduced. The average reprojection errors are reduced from 21.5 pixels to less than 1 pixel.

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► We use the robust trifocal tensor to improve the structure from motion estimation.
► We propose the ROPSO method to improve trifocal tensor estimation.
► Based on the PSO, the ROPSO combines the LMedS with the orthogonal array.
► We show the proposed ROPSO produces more accurate estimation of the trifocal tensor.
► Our method can effectively reduce the reprojection errors of structure from motion.