Exploring Deep Learning for View-Based 3D Model Retrieval
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Summary
This work systematically evaluates the performance of deep learning features in view-based 3D model retrieval on four popular datasets (ETH, NTU60, PSB, and MVRED) by different kinds of similarity measure methods, and it is clear that theseDeep learning features can consistently outperform all of the hand-crafted features, and they are also more robust than the Handcrafted features when different degrees of noise are added into the image.
- Type
- article
- Published
- 2020-02-17
- Cited by
- 115
- References
- 68
- OpenAlex
- https://openalex.org/W3009238340
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:214794083
Keywords
Deep learning, Computer science, Artificial intelligence, Robustness (evolution), Machine learning
References
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- 3D model retrieval using weighted bipartite graph matching
- Shape Discrimination Using Fourier Descriptors
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- 3D model retrieval and classification by semi-supervised learning with content-based similarity
- On Visual Similarity Based 3D Model Retrieval
- 3D model comparison using spatial structure circular descriptor
- A new 3D model retrieval approach based on the elevation descriptor
- Detection of fallen trees in ALS point clouds using a Normalized Cut approach trained by simulation
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- Pairwise View Weighted Graph Network for View-based 3D Model Retrieval
- Anomaly detection in bitcoin information networks with multi-constrained meta path
- 3D Object retrieval based on non-local graph neural networks
- Efficient framework with sequential classification for graphic vehicle identification number recognition
- MVCLN: Multi-View Convolutional LSTM Network for Cross-Media 3D Shape Recognition
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