Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Recognizing corners by fitting parametric models
International Journal of Computer Vision
Object pose from 2-D to 3-D point and line correspondences
International Journal of Computer Vision
A fully projective formulation to improve the accuracy of Lowe's pose-estimation algorithm
Computer Vision and Image Understanding
Adaptive Pose Estimation for Different Corresponding Entities
Proceedings of the 24th DAGM Symposium on Pattern Recognition
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In this paper we investigate the quality of 3D-2D pose estimates using hand labeled line and point correspondences. We select point correspondences from junctions in the image, allowing to construct a meaningful interpretation about how the junction is formed, as proposed in e.g. [1], [2], [3]. We make us of this information referred as the semantic interpretation, to identify the different types of junctions (i.e. L-junctions and T-junctions). T-junctions often denote occluding contour, and thus do not designate a point in space. We show that the semantic interpretations is useful for the removal of these T-junction from correspondence sets, since they have a negative effect on motion estimates. Furthermore, we demonstrate the possibility to derive additional line correspondences from junctions using the semantic interpretation, providing more constraints and thereby more robust estimates.