Single-View Matching Constraints

A single-view matching constraint is described which represents a necessary condition which 6 points in an image must satisfy if they are the images of 6 known 3D points under an arbitrary projective transformation. Similar to the well-known matching constrains for two or more view, represented by fundamental matrices or trifocal tensors, single-view matching constrains are represented by tensors and when multiplied with the homogeneous image coordinates the result vanishes when the condition is satisfied. More precisely, they are represented by 6-th order tensors on R which can be computed in a simple manner from the camera projection matrix and the 6 3D points. The single-view matching constraints can be used for finding correspondences between detected 2D feature points and known 3D points, e.g., on an object, which are observed from arbitrary views. Consequently, this type of constraint can be said to be a representation of 3D shape (in the form of a point set) which is invariant to projective transformations when projected onto a 2D image.