3DPO: A three-dimensional part orientation system
International Journal of Robotics Research
Large Tree Classifier with Heuristic Search and Global Training
IEEE Transactions on Pattern Analysis and Machine Intelligence
Localizing Overlapping Parts by Searching the Interpretation Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Partial Surface and Volume Matching in Three Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Point Signatures: A New Representation for 3D Object Recognition
International Journal of Computer Vision
ACM Computing Surveys (CSUR)
Surface registration by matching oriented points
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
A Multilevel Approach to Sequential Detection of Pictorial Features
IEEE Transactions on Computers
Geometric Probing of Dense Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose sampling for efficient model-based recognition
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
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
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A method is presented for efficient and reliable object recognition within noisy, cluttered, and occluded range images. The method is based on a strategy which hypothesizes the intersection of the object with some selected image point, and searches for additional surface data at locations relative to that point. At each increment, the image is queried for the existence of surface data at a specific spatial location, and the set of possible object poses is further restricted, Eventually, either the object is identified and localized, or the initial hypothesis is refuted.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. The internal tree 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 references 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 process is approximately 3 orders of magnitude more efficient than brute-force template matching, Experimental results are presented in which objects are reliably recognized and localized in 6 dimensions in less than 60 seconds within noisy and significantly occluded range images.