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
Zippered polygon meshes from range images
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
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Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
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An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
ACM Transactions on Graphics (TOG)
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometry and Convergence Analysis of Algorithms for Registration of 3D Shapes
International Journal of Computer Vision
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Pattern Recognition
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Pattern Recognition
Multiview registration for large data sets
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Image Processing
Range image registration using hierarchical segmentation and clustering
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Free form shape registration using the barrier method
Computer Vision and Image Understanding
Accurate overlap area detection using a histogram and multiple closest points
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
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The traditional closest point criterion has been widely used for 3D free form shape matching, object recognition, internet search, computer graphics and medical imaging. However, the rationale of this criterion has not yet been well understood and exploited. In this paper, we apply vector operations and the triangle inequality to carefully analyse this criterion and reveal that this criterion can guarantee that the found point matches satisfy the orientation, rigidity and matching error constraints and thus, are of high relative quality. Such properties not only shed light on and deepen our understanding of this criterion about its generality and practicality and improve our awareness about whether the established point matches are consistent with each other, but also provide us with a possibility to develop novel algorithms for the reliability evaluation of existing point matches and an efficient establishment of more accurate point matches. The experimental results based on real images show that the possible point matches established through extracting and matching spin images often violate these constraints and these constraints can often be successfully applied to reject (probably part of) the unlikely point matches for more accurate free form shape matching results.