An Improved MVCNN for 3D Shape Recognition

The multi-view convolutional neural network architecture represented by MVCNN has achieved great success in 3D shape recognition. Taking the MVCNN architecture as the research goal, this paper proposes a novel 3D shape recognition convolutional neural network Attention-MVCNN that integrates channel attention mechanism, residual structure and Mish activation function. The channel attention machine is used to make the feature extraction network for Attention-MVCNN, which can reduce the feature redundancy caused by traditional convolution. The residual structure can reduce the network over-fitting problem and achieve better gradient information, thereby improving the performance of Attention-MVCNN. We replace the activation function in the Attention-MVCNN network with Mish, a self-regular non-monotonic neural activation function. The smooth activation function allows better information to penetrate the neural network, resulting in better accuracy and generalization. Experiments show that the improved Attention-MVCNN attains the competitive results on ModelNet40 dataset.