3D Model classification based on regnet design space and voting algorithm

3D models are widely used in industrial manufacturing, virtual reality, medical diagnosis and so on. At present, view-based 3D model classification has become an important research topic. However, single view feature can not describe the overall shape of 3D model. When multiple views are fused to describe 3D model, useful information is confused. It causes certain interference to determine 3D model’s category. To solve these problems, a novel method of 3D model classification based on RegNet design space and voting algorithm is proposed. Firstly, 2D views of 3D model are input into RegNet design space with attention mechanism to extract high-level semantic feature(HSF). Secondly, HSF and the corresponding low-level shape features (LSF) of view are fused, including D1, D2, D3, Fourier descriptor, and Zernike moment. Thirdly, LSTM is combined with softmax function to extract more representative features from the fused feature. Finally, based on discriminative features, improved voting algorithm based on shannon entropy is constructed to determine 3D model’s category. Experimental results show that average accuracy of the proposed method on ModelNet10 reaches 94.93%, and the classification performance is outstanding.