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
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Machine Learning
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
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
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
SHREC'11 track: generic shape retrieval
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
SHREC'12 track: generic 3D shape retrieval
EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
Local geometry adaptive manifold re-ranking for shape-based 3D object retrieval
Proceedings of the 20th ACM international conference on Multimedia
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This paper proposes a 3D model retrieval algorithm that employs an unsupervised distance metric learning with a combination of appearance-based features; two sets of local visual features and a set of global features. These visual features are extracted from range images rendered from multiple viewpoints about the 3D model to be compared. The local visual features are bag-of-features histograms of a set of Scale Invariant Feature Transform (SIFT) features by Lowe [7] sampled at either salient or dense and random points. The global visual feature is also a SIFT feature sampled at an image center. The proposed method then uses an unsupervised distance metric learning based on the Manifold Ranking (MR) [15] to compute distances between these features. However, the original MR may not be effective when applied to a set of features having certain distance distribution. We propose an empirical method to adjust the distance profile so that the MR becomes effective. Experiments showed that the retrieval algorithm using a linear combination of distances computed from the proposed set of features by using the modified MR performed well across multiple benchmarks having different characteristics.