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
Four results on randomized incremental constructions
Computational Geometry: Theory and Applications
Registering range views of multipart objects
Computer Vision and Image Understanding
A robust method for registration and segmentation of multiple range images
Computer Vision and Image Understanding
New feature points based on geometric invariants for 3D image registration
International Journal of Computer Vision
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
Model-Based Object Recognition by Geometric Hashing
ECCV '90 Proceedings of the First European Conference on Computer Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Fusing and guiding range measurements with colour video images
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
Registration of 3-D partial surface models using luminance and depth information
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
Surface registration by matching oriented points
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
A Fast Automatic Method for Registration of Partially-Overlapping Range Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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This paper describes a method of automatically performing the registration of two range images that have significant overlap. We first find points of interest in the intensity data that comes with each range image. Then we perform a tetrahedrization of the 3D range points associated with these 2D interest points. The triangle pairs of these tetrahedrizations are then matched in order to compute the registration. The fact that we have 3D data available makes it possible to efficiently prune potential matches. The best match is the one that aligns the largest number of interest points between the two images. The algorithms are demonstrated experimentally on a number of different range image pairs.