The representation, recognition, and locating of 3-d objects
International Journal of Robotics Research
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Estimating 3-D location parameters using dual number quaternions
CVGIP: Image Understanding
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Matching Sets of 3D Line Segments with Application to Polygonal Arc Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching 3-D Line Segments with Applications to Multiple-Object Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure and Motion from Line Segments in Multiple Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guest editorial: Image fusion: Advances in the state of the art
Information Fusion
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Existing algorithms for finding the best match between two sets of 3D lines are not completely satisfactory in the sense that they either yield approximate solutions, or are iterative which means they may not converge to the globally optimal solution. An even more serious shortcoming of the existing algorithms is that they are all non-invariant with respect to the translation of the coordinate system. Thus, any best match found becomes rather meaningless. In this paper, we discuss the source of this non-invariance and present a new algorithm that is invariant to coordinate transforms. Moreover, the algorithm is closed-form which implies that it always yields the best global match.