CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 1995 vol.17 Issue No.01 - January
Issue No.01 - January (1995 vol.17)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.368154
<p><it>Abstract</it>—There are three projective invariants of a set of six points in general position in space. It is well known that these invariants cannot be recovered from one image, however an invariant relationship does exist between space invariants and image invariants. This invariant relationship is first derived for a single image. Then this invariant relationship is used to derive the space invariants, when multiple images are available.</p><p>This paper establishes that the minimum number of images for computing these invariants is three, and the computation of invariants of six points from three images can have as many as three solutions. Algorithms are presented for computing these invariants in closed form.</p><p>The accuracy and stability with respect to image noise, selection of the triplets of images and distance between viewing positions are studied both through real and simulated images.</p><p>Applications of these invariants are also presented. Both the results of Faugeras [<ref rid="BIBP00341" type="bib">1</ref>] and Hartley <it>et al.</it> [<ref rid="BIBP00342" type="bib">2</ref>] for projective reconstruction and Sturm’s method [<ref rid="BIBP00343" type="bib">3</ref>] for epipolar geometry determination from two uncalibrated images with at least seven points are extended to the case of three uncalibrated images with only six points.</p>
This invariant, projective reconstruction, epipolar geometry, uncalibrated images, projective geometry, self-calibration.
Long Quan, "Invariants of Six Points and Projective Reconstruction From Three Uncalibrated Images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.17, no. 1, pp. 34-46, January 1995, doi:10.1109/34.368154