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Circular Motion Geometry Using Minimal Data
June 2004 (vol. 26 no. 6)
pp. 721-731

Abstract—Circular motion or single axis motion is widely used in computer vision and graphics for 3D model acquisition. This paper describes a new and simple method for recovering the geometry of uncalibrated circular motion from a minimal set of only two points in four images. This problem has been previously solved using nonminimal data either by computing the fundamental matrix and trifocal tensor in three images or by fitting conics to tracked points in five or more images. It is first established that two sets of tracked points in different images under circular motion for two distinct space points are related by a homography. Then, we compute a plane homography from a minimal two points in four images. After that, we show that the unique pair of complex conjugate eigenvectors of this homography are the image of the circular points of the parallel planes of the circular motion. Subsequently, all other motion and structure parameters are computed from this homography in a straighforward manner. The experiments on real image sequences demonstrate the simplicity, accuracy, and robustness of the new method.

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Index Terms:
Structure from motion, minimal data, turntable, circular motion, vision geometry, single axis motion.
Citation:
Guang Jiang, Long Quan, Hung-Tat Tsui, "Circular Motion Geometry Using Minimal Data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 721-731, June 2004, doi:10.1109/TPAMI.2004.4
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