  Inspired by the great success achieved by CNN in image recognition,
view-based methods applied CNNs to model the projected views for 3D object
understanding and achieved excellent performance. Nevertheless, multi-view CNN
models cannot model the communications between patches from different views,
limiting its effectiveness in 3D object recognition. Inspired by the recent
success gained by vision Transformer in image recognition, we propose a
Multi-view Vision Transformer (MVT) for 3D object recognition. Since each patch
feature in a Transformer block has a global reception field, it naturally
achieves communications between patches from different views. Meanwhile, it
takes much less inductive bias compared with its CNN counterparts. Considering
both effectiveness and efficiency, we develop a global-local structure for our
MVT. Our experiments on two public benchmarks, ModelNet40 and ModelNet10,
demonstrate the competitive performance of our MVT.
