  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.
