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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2020

08.01.2020

3D Model Retrieval Using Bipartite Graph Matching Based on Attention

verfasst von: Shanlin Sun, Yun Li, Yunfeng Xie, Zhicheng Tan, Xing Yao, Rongyao Zhang

Erschienen in: Neural Processing Letters | Ausgabe 2/2020

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Abstract

In this paper, we propose an attention-based bipartite graph 3D model retrieval algorithm, where many-to-many matching method, the weighted bipartite graph matching, is employed for comparison between two 3D models. Considering the panoramic views can donate the spatial and structural information, in this work, we use panoramic views to represent each 3D model. Attention mechanism is used to generate the weight of all views of each model. And then, we construct a weighted bipartite graph with the views of those models and the weight of each view. According to the bipartite graph, the matching result is used to measure the similarity between two 3D models. We experiment our method on ModelNet, NTU and ETH datasets, and the experimental results and comparison with other methods show the effectiveness of our method.
Vorheriger Artikel Pairwise Generalization Network for Cross-Domain Image Recognition
Nächster Artikel Hierarchical Deep Neural Network for Image Captioning

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Metadaten
Titel
3D Model Retrieval Using Bipartite Graph Matching Based on Attention
verfasst von
Shanlin Sun
Yun Li
Yunfeng Xie
Zhicheng Tan
Xing Yao
Rongyao Zhang
Publikationsdatum
08.01.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-10155-0

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