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|a 10.1109/TIP.2020.3048623
|2 doi
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|a eng
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| 100 |
1 |
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|a Liu, Xinhai
|e verfasserin
|4 aut
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| 245 |
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|a Fine-Grained 3D Shape Classification With Hierarchical Part-View Attention
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|c 2021
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|a Text
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|a Date Revised 15.01.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Fine-grained 3D shape classification is important for shape understanding and analysis, which poses a challenging research problem. However, the studies on the fine-grained 3D shape classification have rarely been explored, due to the lack of fine-grained 3D shape benchmarks. To address this issue, we first introduce a new 3D shape dataset (named FG3D dataset) with fine-grained class labels, which consists of three categories including airplane, car and chair. Each category consists of several subcategories at a fine-grained level. According to our experiments under this fine-grained dataset, we find that state-of-the-art methods are significantly limited by the small variance among subcategories in the same category. To resolve this problem, we further propose a novel fine-grained 3D shape classification method named FG3D-Net to capture the fine-grained local details of 3D shapes from multiple rendered views. Specifically, we first train a Region Proposal Network (RPN) to detect the generally semantic parts inside multiple views under the benchmark of generally semantic part detection. Then, we design a hierarchical part-view attention aggregation module to learn a global shape representation by aggregating generally semantic part features, which preserves the local details of 3D shapes. The part-view attention module hierarchically leverages part-level and view-level attention to increase the discriminability of our features. The part-level attention highlights the important parts in each view while the view-level attention highlights the discriminative views among all the views of the same object. In addition, we integrate a Recurrent Neural Network (RNN) to capture the spatial relationships among sequential views from different viewpoints. Our results under the fine-grained 3D shape dataset show that our method outperforms other state-of-the-art methods. The FG3D dataset is available at https://github.com/liuxinhai/FG3D-Net
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|a Journal Article
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| 700 |
1 |
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|a Han, Zhizhong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Yu-Shen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zwicker, Matthias
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 20., Seite 1744-1758
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|x 1941-0042
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|g volume:30
|g year:2021
|g day:20
|g pages:1744-1758
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|u http://dx.doi.org/10.1109/TIP.2020.3048623
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