Title
Three-Dimensional Reconstruction of Points and Lines with Unknown Correspondence across Images
Abstract
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.
Year
DOI
Venue
2001
10.1023/A:1012424014764
International Journal of Computer Vision
Keywords
Field
DocType
feature correspondence matching,point/line affinity measure,weighted bipartite graph matching,maximum network flow
Computer vision,Line segment,Pattern recognition,Computer science,Bipartite graph,Triangulation (social science),Artificial intelligence,Monocular,3-dimensional matching,Graph (abstract data type)
Journal
Volume
Issue
ISSN
45
2
1573-1405
Citations 
PageRank 
References 
4
1.40
37
Authors
5
Name
Order
Citations
PageRank
Y.-Q. Cheng141.40
X. G. Wang241.40
R. T. Collins3193.48
E. M. Riseman41402458.95
A. Hanson51348304.11