Y.Q.Cheng, X.G.Wang, R.T.Collins, E.Riseman and A.Hanson,
"Three-Dimensional Reconstruction of Points and Lines with
Unknown Correspondence across Images,"
International Journal of Computer Vision, Vol 45(2), Nov 2001, pp.129-156.
Three-dimensional reconstruction from a set of images is an important and
difficult problem in computer vision. In this paper, we address the problem
of determining image feature correspondences while simultaneously
reconstructing the corresponding 3D features, given the camera poses of
disparate monocular views. First, two new affinity measures are presented
that capture the degree to which candidate features from different images
consistently represent the projection of the same 3D point or 3D line. An
affinity measure for point features in two different views is defined with
respect to their distance from a hypothetical projected 3D
pseudo-intersection point. Similarly, an affinity measure for 2D image line
segments across three views is defined with respect to a 3D
pseudo-intersection line. These affinity measures provide a foundation for
determining unknown correspondences using weighted bipartite graphs
representing candidate point and line matches across different images. As
a result of this graph representation, a standard graph-theoretic algorithm
can provide an optimal, simultaneous matching and triangulation of points
across two views, and lines across three views. Experimental results on
synthetic and real data demonstrate the effectiveness of the approach.