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MVPN: Multi-View Prototype Network for 3D Shape Recognition
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DOI：10.1109/ACCESS.2019.2937489.png)
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Abstract
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Convolutional Neural Networks (CNNs) have been widely used for 3D shape recognition. Despite its significant performance, we point out that there is still some rooms for improvement, including the lack of sufficient training samples and the extraction of discriminative features. In this work, we address the above limitations by suggesting a novel end-to-end learning framework named Multi-Veiw Prototype Network (MVPN). MVPN can jointly learn the features from multiple views of 3D shapes and the prototype representations of each class, recognition can then be performed by finding the nearest class prototype in the embedding space for a query sample. Furthermore, to obtain more discriminative features, we propose a discriminative loss, which encourages intra-class compactness and inter-class separability between learned representations, making the representation more discriminative and robust. Extensive experiments are conducted on two benchmarks: ModelNet dataset and ShapeNet Core55 dataset, and superior results have been achieved compared with state-of-the-art approaches.
Keywords:
3D shape recognition
prototype network
discriminative feature learning
convolutional neural networks
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