Efficient Hierarchical Triplet Merging for Camera Pose Estimation

This paper deals with efficient means for camera pose estimation for difficult scenes. Particularly, we speed up the combination of image triplets to image sets by hierarchical merging and a reduction of the number of merged points. By image sets we denote a generalization of image sequences where images can be linked in multiple directions, i.e., they can form a graph. To obtain reliable results for triplets, we use large numbers of corresponding points. For a high-quality and yet efficient merging of the triplets we propose strategies for the reduction of the number of points. The strategies are evaluated based on statistical measures employing the full covariance information for the camera poses from bundle adjustment. We show that to obtain a statistically sound result, intuitively appealing deterministic reduction strategies are problematic and that a simple reduction strategy based on random deletion was evaluated best. We also discuss the benefits of the evaluation measures for finding conceptual and implementation weaknesses. The paper is illustrated with a number of experiments giving standard deviations for all values.