3D shape retrieval by Poisson histogram
Pattern Recognition Letters
SHREC'12 track: generic 3D shape retrieval
EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
3D model retrieval using hybrid features and class information
Multimedia Tools and Applications
A comparison of methods for sketch-based 3D shape retrieval
Computer Vision and Image Understanding
SHREC'13 track: retrieval of objects captured with low-cost depth-sensing cameras
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
SHREC'13 track: large scale sketch-based 3D shape retrieval
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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We propose a new method of similarity search for 3D shape models, given an arbitrary 3D shape as a query. The method features the high search performance enabled in part by our unique feature vector called Multi-Fourier Spectra Descriptor (MFSD), and in part by augmenting the feature vector with spectral clustering. The MFSD is composed of four independent Fourier spectra with periphery enhancement. It allows us to faithfully capture the inherent characteristics of an arbitrary 3D shape object regardless of the dimension, orientation, and original location of the object when it is first defined. Given a 3D shape database, the augmentation with spectral clustering is done first by computing the p-minimum spanning tree of the whole data set, where p is a number usually much less than m, the size of the whole 3D shape data set. We then define the affinity matrix, which is a square matrix of size m by m, where each element of the matrix denotes the distance between two shape objects. The distance is computed in advance by traversing the p-minimum spanning tree. The eigenvalue decomposition is then applied to the affinity matrix to reduce dimensionality of the matrix, followed by grouping into k clusters. The cluster information is kept for augmenting the search performance when a query is given. With a series of benchmark data sets, we will demonstrate that our approach outperforms previously known methods for 3D shape retrieval.