Significant problems with a standard shape based approach to object pose estimation are the size of the database needed and the time it takes to search this database. The problems are compounded with highly symmetric objects whose internal detail must be considered for accurate pose determination. Our approach to object pose estimation is based on the reciprocal basis image set. The method is related to the one that uses a suite of gray-scale images of object for analytical object model representation. By representing the object as a Fourier series using a finite sample set of poses, the object model consisting of a reciprocal basis image set can be determined. When an input image is projected onto the reciprocal image basis, an estimate for the object pose can be obtained from the phase of a complex exponential. This allows a suite of images rotated about an axis to be represented by a reciprocal basis image set. Pose estimation about this axis can then be determined by a linear projection of the input object onto this reciprocal basis. The internal detail of the image is thus maintained through a finite reciprocal basis image set, allowing for accurate post estimation of highly symmetric objects. This method can decrease both computation time and disk space from many standard shape based approaches. Examples illustrating the highly accurate performance of object pose estimation using synthetic armor images based on the reciprocal basis approach are given.
C. Y. Chang,
"Pose estimation in automatic object recognition", Proc. SPIE 2752, Optical Pattern Recognition VII, (15 March 1996); doi: 10.1117/12.235655; https://doi.org/10.1117/12.235655