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Multi-Modality Learning for Non-Rigid 3D Shape Retrieval via Structured Sparsity Regularizations
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DOI：10.1109/JSEN.2021.3094122.png)
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摘要
En 中文
Big challenges are usually occurring in non-rigid 3D shape retrieval, for the shapes undergoing arbitrarily non-affine transformations. In this work, a novel design of feature learning approach is proposed for non-rigid 3D shape retrieval, dubbed Structured Sparsity Regularized Multi-Modality Method (SSR-MM). The shape signatures which capture the deformation-invariant characteristics are averaged and stacked for a multi-modality machine learning approach, and a transform matrix based on the structure sparsity regularization is utilized to map those signatures obtaining the discriminative features for retrieval. The proposed framework is evaluated on the publicly available non-rigid 3D human benchmarks, and the experimental results show the efficacy of our contributions and the advantages of our method over existing ones.
Keyword:
Shape
Three-dimensional displays
Manifolds
Kernel
Sensors
Strain
Eigenvalues and eigenfunctions
3D shape retrieval
multi-modality learning
non-rigid shapes
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4.5
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2.2W
被引数:
7.3W
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