International Journal of Computer Vision
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Transactions on Graphics (TOG)
Nearest Neighbor Classification in 3D Protein Databases
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Skeleton Based Shape Matching and Retrieval
SMI '03 Proceedings of the Shape Modeling International 2003
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
SMI '04 Proceedings of the Shape Modeling International 2004
Laplace-spectra as fingerprints for shape matching
Proceedings of the 2005 ACM symposium on Solid and physical modeling
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Salient geometric features for partial shape matching and similarity
ACM Transactions on Graphics (TOG)
Spherical Wavelet Descriptors for Content-based 3D Model Retrieval
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Enhanced 2D/3D Approaches Based on Relevance Index for 3D-Shape Retrieval
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
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
PTK: A novel depth buffer-based shape descriptor for three-dimensional object retrieval
The Visual Computer: International Journal of Computer Graphics
A new 3D model retrieval approach based on the elevation descriptor
Pattern Recognition
Distinctive regions of 3D surfaces
ACM Transactions on Graphics (TOG)
Partial matching of 3D shapes with priority-driven search
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Retrieving articulated 3-D models using medial surfaces
Machine Vision and Applications
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
Numerical Geometry of Non-Rigid Shapes
Numerical Geometry of Non-Rigid Shapes
Spatially Enhanced Bags of Words for 3D Shape Retrieval
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
A New Shape Benchmark for 3D Object Retrieval
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Local feature extraction and matching on range images: 2.5D SIFT
Computer Vision and Image Understanding
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
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Spectral-Driven Isometry-Invariant Matching of 3D Shapes
International Journal of Computer Vision
International Journal of Computer Vision
A 3D Shape Retrieval Framework Supporting Multimodal Queries
International Journal of Computer Vision
PANORAMA: A 3D Shape Descriptor Based on Panoramic Views for Unsupervised 3D Object Retrieval
International Journal of Computer Vision
Visual Similarity Based 3D Shape Retrieval Using Bag-of-Features
SMI '10 Proceedings of the 2010 Shape Modeling International Conference
Shape google: Geometric words and expressions for invariant shape retrieval
ACM Transactions on Graphics (TOG)
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Isometric deformation invariant 3D shape recognition
Pattern Recognition
On bending invariant signatures for surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of methods for non-rigid 3D shape retrieval
Pattern Recognition
Visual vocabulary signature for 3D object retrieval and partial matching
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
SHREC'10 track: generic 3D warehouse
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
SHREC'11 track: shape retrieval on non-rigid 3D watertight meshes
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
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Content-based 3D object retrieval has become an active topic in many research communities. In this paper, we propose a novel visual similarity-based 3D shape retrieval method (CM-BOF) using Clock Matching and Bag-of-Features. Specifically, pose normalization is first applied to each object to generate its canonical pose, and then the normalized object is represented by a set of depth-buffer images captured on the vertices of a given geodesic sphere. Afterwards, each image is described as a word histogram obtained by the vector quantization of the image's salient local features. Finally, an efficient multi-view shape matching scheme (i.e., Clock Matching) is employed to measure the dissimilarity between two models. When applying the CM-BOF method in non-rigid 3D shape retrieval, multidimensional scaling (MDS) should be utilized before pose normalization to calculate the canonical form for each object. This paper also investigates several critical issues for the CM-BOF method, including the influence of the number of views, codebook, training data, and distance function. Experimental results on five commonly used benchmarks demonstrate that: (1) In contrast to the traditional Bag-of-Features, the time-consuming clustering is not necessary for the codebook construction of the CM-BOF approach; (2) Our methods are superior or comparable to the state of the art in applications of both rigid and non-rigid 3D shape retrieval.