Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation and recognition of surface shapes in range images: a differential geometry approach
Computer Vision, Graphics, and Image Processing
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of free-form object representation and recognition techniques
Computer Vision and Image Understanding
Hierarchical Part-Based Visual Object Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical models for visual object recognition and tracking
Graphical models for visual object recognition and tracking
Multi-scale features for approximate alignment of point-based surfaces
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
A Probabilistic Framework for 3D Visual Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast point feature histograms (FPFH) for 3D registration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Object recognition and full pose registration from a single image for robotic manipulation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Probabilistic object models for pose estimation in 2D images
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
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We present a 3D, probabilistic object-surface model, along with mechanisms for probabilistically integrating unregistered 2.5D views into the model, and for segmenting model instances in cluttered scenes. The object representation is a probabilistic expression of object parts through smooth surface-point distributions obtained by kernel density estimation on 3D point clouds. A multi-part, viewpoint-invariant model is learned incrementally from a set of roughly segmented, unregistered views, by sequentially registering and fusing the views with the incremental model. Registration is conducted by nonparametric inference of maximum-likelihood model parameters, using Metropolis-Hastings MCMC with simulated annealing. The learning of viewpoint-invariant models and the applicability of our method to pose estimation, object detection, and object recognition is demonstrated on 3D-scan data, providing qualitative, quantitative and comparative evaluations.