Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D zernike descriptors for content based shape retrieval
SM '03 Proceedings of the eighth ACM symposium on Solid modeling and applications
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
ACM SIGGRAPH 2005 Papers
Salient geometric features for partial shape matching and similarity
ACM Transactions on Graphics (TOG)
Hierarchical mesh segmentation based on fitting primitives
The Visual Computer: International Journal of Computer Graphics
Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
3D Building Detection and Modeling from Aerial LIDAR Data
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Shape Topics: A Compact Representation and New Algorithms for 3D Partial Shape Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Distinctive regions of 3D surfaces
ACM Transactions on Graphics (TOG)
Probabilistic fingerprints for shapes
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Partial matching of 3D shapes with priority-driven search
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Surface matching with salient keypoints in geodesic scale space
Computer Animation and Virtual Worlds - CASA'2008 Special Issue
Spatially Enhanced Bags of Words for 3D Shape Retrieval
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Salient spectral geometric features for shape matching and retrieval
The Visual Computer: International Journal of Computer Graphics
Dense sampling and fast encoding for 3D model retrieval using bag-of-visual features
Proceedings of the ACM International Conference on Image and Video Retrieval
Learning the Compositional Nature of Visual Object Categories for Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thesaurus-based 3D Object Retrieval with Part-in-Whole Matching
International Journal of Computer Vision
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
Part analogies in sets of objects
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Visual vocabulary signature for 3D object retrieval and partial matching
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
A 3D shape benchmark for retrieval and automatic classification of architectural data
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
Improving 3D similarity search by enhancing and combining 3D descriptors
Multimedia Tools and Applications
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While approaches based on local features play a more and more important role for 3D shape retrieval, the problems of feature selection and similarity measurement between sets of local features still remain open tasks. Common algorithms usually measure the similarity between two such sets by either establishing feature correspondences or by using Bag-of-Features (BoF) approaches. While establishing correspondences often involves a lot of manually chosen thresholds, BoF approaches can hardly model the spatial structure of the underlying 3D object. In this paper focusing on retrieval of 3D models representing man-made objects, we try to tackle both of these problems. Exploiting the fact that man-made objects usually consist of a small set of certain shape primitives, we propose a feature selection technique that decomposes 3D point clouds into sections that can be represented by a plane, a sphere, a cylinder, a cone, or a torus. We then introduce a probabilistic framework for analyzing and learning the spatial arrangement of the detected shape primitives with respect to training objects belonging to certain categories. The knowledge acquired in this learning process allows for efficient retrieval and classification of new 3D objects. We finally evaluate our algorithm on the recently introduced 3D Architecture Shape Benchmark, which mainly consists of 3D models representing man-made objects.