3D Model Retrieval with Spherical Harmonics and Moments

By Dietmar Saupe, Dejan V. Vranic. DAGM-Symposium, 2001

329 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4069ca591433b1476f549f817a6ce64bf337b8b7

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In Aerospace Engineering

In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Deepshape: Deep learned shape descriptor for 3D shape matching and retrieval (2015, background): “In the past decades, plenty of shape descriptors have been proposed, such as theD2 shape distribution [12], statistical moments of the model [31, 24], Fourier descriptor [8] and Eigenvalue Descriptor (EVD)[15].”
Hough Transform and 3D SURF for Robust Three Dimensional Classification (2010, background): “Examples are Fourier or spherical harmonics [1, 2], shape moments [2], shape histograms [3].”
Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors (2003, background): “Examples of such descriptors have included: The Extended Gaussian Image 4 , which describes the distribution of normals across the surface of the model Shape Histograms 5 , which describe the distribution of points on the model across all rays from the origin Spherical Extent Functions 6 , which des”
The Princeton Shape Benchmark (2004, unknown): “…the differences be-tween twelve shape descriptors, including D2 shape distributions [23], Extended Gaussian Images [13, 15], Shape Histograms [1], Spherical Extent Functions [27, 35], Gaussian Euclidean Distance Transforms [16], Spherical Harmonic Descriptors [16], and Light Field Descriptors [4].”
3D deep shape descriptor (2015, background): “, D2 shape distribution [36], statistical moments [15], Fourier descriptor [49, 42], Light Field Descriptor [10], Eigenvalue Descriptor [25], etc.”
Human activity recognition using multi-features and multiple kernel learning (2014, methodology): “Over the last decade, spherical harmonics coefficients have been applied to several computer vision applications such as 3-D shape descriptors [35] as well as 3D model retrieval [36], medical image analysis [34] and rotation estimation [37].”
Three-dimensional shape searching: state-of-the-art review and future trends (2005, unknown): “Vranic and Saupe [96,97] developed a 3D shape descriptor based on spherical harmonics.”
Supervised learning of hidden and non-hidden 0-order affordances and detection in real scenes (2012, unknown): “Spherical Extent Descriptor (SEE) [16] — Our”
A search engine for 3D models (2003, methodology): “Others have used spherical harmonics to obtain multiresolution representations of shape [Saupe and Vrani 2001; Vranic et al. 2001], however, they require a priori registration with principal axes.”