Robust line-based pose enumeration from a single image

The authors propose robust algorithms for line-based pose enumeration from a single view and report on their evaluations by simulations. The proposed algorithms incorporate two major refinements into the algorithms originally proposed by T. Shakunaga. The first refinement, introduction of zone-crossing detection to the 1-D search, decreases the rate of overlooking a correct pose. The second refinement, adaptive selection of a perspective angle transform pair, considerably reduces the average estimation error. Simulation results show that pose estimation precision depends primarily on the precision of line detection. Although the refinements are effective, they are better for more precise line detection. For 99% of rigid body samples, the algorithm can estimate rotation with an error of less than 2°, and for 99.9% of the samples, the error is less than 10°. Simulation experiments for articulated objects show similar results by using the second algorithm