Structural Indexing: Efficient 3-D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
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
International Journal of Computer Vision
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
ICP Registration Using Invariant Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Transactions on Graphics (TOG)
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Shape Matching: Similarity Measures and Algorithms
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
A Survey of Content Based 3D Shape Retrieval Methods
SMI '04 Proceedings of the Shape Modeling International 2004
Parts-based 3D object classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
Identification of highly similar 3d objects using model saliency
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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This paper proposes a novel method to exploit model similarity in model-based 3D object recognition. The scenario consists of a large 3D model database of vehicles, and rapid indexing and matching needs to be done without sequential model alignment. In this scenario, the competition amongst shape features from similar models may pose serious challenge to recognition. To solve the problem, we propose to use a metric to quantitatively measure model similarities. For each model, we use similarity measures to define a model-centric class (MCC), which contains a group of similar models and the pose transformations between the model and its class members. Similarity information embedded in a MCC is used to boost matching hypotheses generation so that the correct model gains more opportunities to be hypothesized and identified. The algorithm is implemented and extensively tested on 1100 real LADAR scans of vehicles with a model database containing over 360 models.