A Solution for the Registration of Multiple 3D Point Sets Using Unit Quaternions
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Content-Based Image Retrieval Based on Local Affinely Invariant Regions
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
3d object modeling and recognition in photographs and video
3d object modeling and recognition in photographs and video
International Journal of Computer Vision
Integrating multiple model views for object recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
MESH-based active Monte Carlo recognition (MESH-AMCR)
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Representing images of a rotating object with cyclic permutation for view-based pose estimation
Computer Vision and Image Understanding
A probabilistic framework for object search with 6-DOF pose estimation
International Journal of Robotics Research
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Local appearance models in the neighborhood of salient image features, together with local and/or global geometric constraints, serve as the basis for several recent and effective approaches to 3D object recognition from photographs. However, these techniques typically either fail to explicitly account for the strong geometric constraints associated with multiple images of the same 3D object, or require a large set of training images with much overlap to construct relatively sparse object models. This paper proposes a simple new method for automatically constructing 3D object models consisting of dense assemblies of small surface patches and affine-invariant descriptions of the corresponding texture patterns from a few (7 to 12) stereo pairs. Similar constraints are used to effectively identify instances of these models in highly cluttered photographs taken from arbitrary and unknown viewpoints. Experiments with a dataset consisting of 80 test images of 9 objects, including comparisons with a number of baseline algorithms, demonstrate the promise of the proposed approach.