Multi-view convolutional vision transformer for 3D object recognition |
| |
Affiliation: | 1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;2. Engineering Research Center of Mine Digitization, Ministry of Education of the Peoples Republic of China, Xuzhou 221116, China;3. Disaster Intelligent Prevention and Control and Emergency Rescue Innovation Research Center, Xuzhou 221116, China;4. Jiangsu Junsheng Wanbang Holding Group Co., Ltd., Xuzhou 221116, China;5. School of Electrical Engineering and Computer Science, University of Ottawa, Canada |
| |
Abstract: | With the rapid development of three-dimensional (3D) vision technology and the increasing application of 3D objects, there is an urgent need for 3D object recognition in the fields of computer vision, virtual reality, and artificial intelligence robots. The view-based method projects 3D objects into two-dimensional (2D) images from different viewpoints and applies convolutional neural networks (CNN) to model the projected views. Although these methods have achieved excellent recognition performance, there is not sufficient information interaction between the features of different views in these methods. Inspired by the recent success achieved by vision transformer (ViT) in image recognition, we propose a hybrid network by taking advantage of CNN to extract multi-scale local information of each view, and of transformer to capture the relevance of multi-scale information between different views. To verify the effectiveness of our multi-view convolutional vision transformer (MVCVT), we conduct experiments on two public benchmarks, ModelNet40 and ModelNet10, and compare with those of some state-of-the-art methods. The final results show that MVCVT has competitive performance in 3D object recognition. |
| |
Keywords: | Multi-view 3D object recognition Feature fusion Convolutional neural networks |
本文献已被 ScienceDirect 等数据库收录! |
|