Editing Scale-invariant feature transform (section)

=== 3D scene modeling, recognition and tracking ===
This application uses SIFT features for [[3D single-object recognition|3D object recognition]] and [[3D modeling]] in context of [[augmented reality]], in which synthetic objects with accurate pose are superimposed on real images. SIFT matching is done for a number of 2D images of a scene or object taken from different angles. This is used with [[bundle adjustment]] initialized from an [[essential matrix]] or [[trifocal tensor]] to build a sparse 3D model of the viewed scene and to simultaneously recover camera poses and [[Geometric camera calibration|calibration]] parameters. Then the position, orientation and size of the virtual object are defined relative to the coordinate frame of the recovered model. For online [[match moving]], SIFT features again are extracted from the current video frame and matched to the features already computed for the world model, resulting in a set of 2D-to-3D correspondences. These correspondences are then used to compute the current camera pose for the virtual projection and final rendering. A regularization technique is used to reduce the jitter in the virtual projection.<ref name="Gordon2006" /> The use of SIFT directions have also been used to increase robustness of this process.<ref name="SIFTOrientationTrifocal" /><ref name="SIFTOrientationPose" />  3D extensions of SIFT have also been evaluated for [[true 3D]] object recognition and retrieval.<ref name=Flitton2010 /><ref name="flitton13interestpoint">{{cite journal| author=Flitton, G.T., Breckon, T.P., Megherbi, N.| title=A Comparison of 3D Interest Point Descriptors with Application to Airport Baggage Object Detection in Complex CT Imagery| journal=Pattern Recognition| volume=46| issue=9| pages=2420–2436| year=2013| doi=10.1016/j.patcog.2013.02.008| bibcode=2013PatRe..46.2420F| hdl=1826/15213| hdl-access=free}}</ref>