K-d trees for semidynamic point sets
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
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
A Simple Algorithm for Nearest Neighbor Search in High Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Point Signatures: A New Representation for 3D Object Recognition
International Journal of Computer Vision
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
View-Based Recognition Using an Eigenspace Approximation to the Hausdorff Measure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Models of Appearance for 3-D Object Recognition
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Registering Multiview Range Data to Create 3D Computer Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Properties of Embedding Methods for Similarity Searching in Metric Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface registration by matching oriented points
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Fast and accurate shape-based registration
Fast and accurate shape-based registration
A Similarity-Based Aspect-Graph Approach to 3D Object Recognition
International Journal of Computer Vision
SMI '04 Proceedings of the Shape Modeling International 2004
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel Programming in C with MPI and OpenMP
Parallel Programming in C with MPI and OpenMP
Probabilistic Object Recognition Using Multidimensional Receptive Field Histograms
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Rapid and brief communication: The LLE and a linear mapping
Pattern Recognition
Model-Based Tracking by Classification in a Tiny Discrete Pose Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Predetermination of ICP Registration Errors And Its Application to View Planning
3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
Pose Determination By PotentialWell Space Embedding
3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Linear model hashing and batch RANSAC for rapid and accurate object recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Point fingerprint: a new 3-D object representation scheme
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Performance Evaluation of 3D Keypoint Detectors
International Journal of Computer Vision
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A novel object recognition algorithm is introduced to identify objects and recover their pose from sparse range data. The method is based upon comparing the 7-D error surfaces of objects in various poses, which result from the registration error function between two convolved surfaces. The objects and their pose values are encoded by a small set of feature vectors extracted from the minima of the error surfaces. The problem of object recognition is thus reduced to comparing these feature vectors to find the corresponding error surfaces between the runtime data and a preprocessed database.The algorithm, called Potential Well Space Embedding (PWSE) has been implemented and tested on both simulated and real data. The experimental results show the technique to be both effective and efficient, executing at 122 frames per second on standard hardware and with recognition rates exceeding 97% for a database of 60 objects. The performance of PWSE on the large size database was also evaluated on the Princeton Shape Benchmark containing 1,814 objects. In addition, it functions well with very sparse data, possibly comprising only hundreds of points per image, and is shown to be robust to measurement error and outliers.With some small modifications, we applied PWSE to the problem of object class recognition. In experiments with the Princeton Shape Benchmark, PWSE is able to provides better classification rates than the previous methods in terms of nearest neighbour classification.