Title: A novel neural network-based 3D animation model classification method

Authors: Ximan Shi

Addresses: Xinxiang University, Henan, Xinxiang, China

Abstract: The rapid development of information technology has also brought new vitality to art design. The 3D animation model making is a new multimedia technology based on computer technology. In order to efficiently organise and utilise the 3D model resources, researchers focus on how to achieve effective retrieval and classification. In order to realise the recognition and classification of 3D models, a novel network model called 3DSmallPCapsNet is proposed in this paper based on the feature that Capsule Network (CapsNet) exploits vector neurons to store feature space information. The proposed method can extract more representative features while reducing the model complexity. To evaluate our method, three different methods which are MeshNet, Shape-DNA and GPS-embedding, are compared. The experimental results on data sets SHREC10 and SHREC15 show that the proposed method has better performance.

Keywords: 3D model classification; CapsNet; capsule network; pooling; animation model.

DOI: 10.1504/IJCAT.2023.132096

International Journal of Computer Applications in Technology, 2023 Vol.71 No.3, pp.222 - 228

Received: 06 Apr 2022
Received in revised form: 27 Apr 2022
Accepted: 02 May 2022

Published online: 11 Jul 2023 *

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