Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indexing without Invariants in 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic 3D Object Recognition
International Journal of Computer Vision
3D object recognition and pose with relational indexing
Computer Vision and Image Understanding
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Similarity-Based Aspect-Graph Approach to 3D Object Recognition
International Journal of Computer Vision
SoftPOSIT: Simultaneous Pose and Correspondence Determination
International Journal of Computer Vision
Object Recognition in High Clutter Images Using Line Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Hidden Markov Model approach for appearance-based 3D object recognition
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dependable 3D object recognition with two-layered particle filter
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
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We present a probabilistic 3D object recognition approach using multiple interpretations generation in cluttered domestic environment. How to handle pose ambiguity and uncertainty is the main challenge in most recognition systems. In our approach, invariant 3D lines are employed to generate the pose hypotheses as multiple interpretations, especially ambiguity from partial occlusion and fragment of 3D lines are taken into account. And the estimated pose is represented as a region instead of a point in pose space by considering the measurement uncertainties. Then, probability of each interpretation is computed reliably using Bayesian principle in terms of both likelihood and unlikelihood. Finally, fusion strategy is applied to a set of top ranked interpretations, which are further verified and refined to make more accurate pose estimation in real time. The experimental results support the potential of the proposed approach in the real cluttered domestic environment.