PVFAN: Point-view fusion attention network for 3D shape recognition

What is it about?

3D shape recognition is a critical research topic in the field of computer vision, attracting substantial attention. Existing approach-es mainly focus on extracting distinctive 3D shape features; however, they often neglect the model's robustness and lack refine-ment in deep features. To address these limitations, we propose the point-view fusion attention network that aims to extract a concise, informative, and robust 3D shape descriptor. Initially, our approach combines multi-view features with point cloud fea-tures to obtain accurate and distinguishable fusion features. To effectively handle these fusion features, we design a dual-attention convolutional network which consists of a channel attention module and a spatial attention module. This dual-attention mechanism greatly enhances the generalization ability and robustness of 3D recognition models. Notably, we introduce a strip-pooling layer in the channel attention module to refine the features, resulting in improved fusion features that are more compact. Finally, a clas-sification process is performed on the refined features to assign appropriate 3D shape labels. Our extensive experiments on the ModelNet10 and ModelNet40 datasets for 3D shape recognition and retrieval demonstrate the remarkable accuracy and robust-ness of the proposed method.

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Photo by Luke Jones on Unsplash

Photo by Luke Jones on Unsplash

Why is it important?

3D shape recognition is a critical research topic in the field of computer vision, attracting substantial attention. Existing approach-es mainly focus on extracting distinctive 3D shape features; however, they often neglect the model's robustness and lack refine-ment in deep features. To address these limitations, we propose the point-view fusion attention network that aims to extract a concise, informative, and robust 3D shape descriptor. Initially, our approach combines multi-view features with point cloud fea-tures to obtain accurate and distinguishable fusion features. To effectively handle these fusion features, we design a dual-attention convolutional network which consists of a channel attention module and a spatial attention module. This dual-attention mechanism greatly enhances the generalization ability and robustness of 3D recognition models. Notably, we introduce a strip-pooling layer in the channel attention module to refine the features, resulting in improved fusion features that are more compact. Finally, a clas-sification process is performed on the refined features to assign appropriate 3D shape labels. Our extensive experiments on the ModelNet10 and ModelNet40 datasets for 3D shape recognition and retrieval demonstrate the remarkable accuracy and robust-ness of the proposed method.