Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
3DPO: A three-dimensional part orientation system
International Journal of Robotics Research
The representation, recognition, and locating of 3-d objects
International Journal of Robotics Research
Localizing Overlapping Parts by Searching the Interpretation Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Algorithms
Recognizing 3-D Objects Using Surface Descriptions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determination of Three-Dimensional Object Location and Orientation from Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Object Recognition Using Bipartite Matching Embedded in Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
BONSAI: 3D Object Recognition Using Constrained Search
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
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
Point Signatures: A New Representation for 3D Object Recognition
International Journal of Computer Vision
ACM Computing Surveys (CSUR)
Recognizing Objects by Matching Oriented Points
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The Sample Tree: A Sequential Hypothesis Testing Approach to 3D Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Surface registration by matching oriented points
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
The representation of discrete functions by decision trees: aspects of complexity and problems of testing
Geometric probing for 3d object recognition in dense range data
Geometric probing for 3d object recognition in dense range data
Efficient and reliable template set matching for 3D object recognition
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
View planning for automated three-dimensional object reconstruction and inspection
ACM Computing Surveys (CSUR)
Model-Based Tracking by Classification in a Tiny Discrete Pose Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast pattern recognition using normalized grey-scale correlation in a pyramid image representation
Machine Vision and Applications
Fast and automatic object pose estimation for range images on the GPU
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
Local shape descriptor selection for object recognition in range data
Computer Vision and Image Understanding
Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data
International Journal of Computer Vision
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A new method is presented for the efficient and reliable pose determination of 3D objects in dense range image data. The method is based upon a minimalistic Geometric Probing strategy that hypothesizes the intersection of the object with some selected image point, and searches for additional surface data at locations relative to that point. The strategy is implemented in the discrete domain as a binary decision tree classifier. The tree leaf nodes represent individual voxel templates of the model, with one template per distinct model pose. The internal nodes represent the union of the templates of their descendant leaf nodes. The union of all leaf node templates is the complete template set of the model over its discrete pose space. Each internal node also encodes a single voxel which is the most common element of its child node templates. Traversing the tree is equivalent to efficiently matching the large set of templates at a selected image seed location. The method was implemented and extensive experiments were conducted for a variety of combinations of tree designs and traversals under isolated, cluttered, and occluded scene conditions. The results demonstrated a trade-off between efficiency and reliability. It was concluded that there exist combinations of tree design and traversal which are both highly efficient and reliable.