MANet: Multimodal Attention Network based Point- View fusion for 3D Shape Recognition

• URL: http://arxiv.org/abs/2002.12573v1
• Date: Fri, 28 Feb 2020 07:00:14 GMT
• Title: MANet: Multimodal Attention Network based Point- View fusion for 3D Shape Recognition
• Authors: Yaxin Zhao, Jichao Jiao and Tangkun Zhang
• Abstract summary: This paper proposes a fusion network based on multimodal attention mechanism for 3D shape recognition. Considering the limitations of multi-view data, we introduce a soft attention scheme, which can use the global point-cloud features to filter the multi-view features. More specifically, we obtain the enhanced multi-view features by mining the contribution of each multi-view image to the overall shape recognition.
• Score: 0.5371337604556311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D shape recognition has attracted more and more attention as a task of 3D vision research. The proliferation of 3D data encourages various deep learning methods based on 3D data. Now there have been many deep learning models based on point-cloud data or multi-view data alone. However, in the era of big data, integrating data of two different modals to obtain a unified 3D shape descriptor is bound to improve the recognition accuracy. Therefore, this paper proposes a fusion network based on multimodal attention mechanism for 3D shape recognition. Considering the limitations of multi-view data, we introduce a soft attention scheme, which can use the global point-cloud features to filter the multi-view features, and then realize the effective fusion of the two features. More specifically, we obtain the enhanced multi-view features by mining the contribution of each multi-view image to the overall shape recognition, and then fuse the point-cloud features and the enhanced multi-view features to obtain a more discriminative 3D shape descriptor. We have performed relevant experiments on the ModelNet40 dataset, and experimental results verify the effectiveness of our method.

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