Web3D learning framework for 3D shape retrieval based on hybrid convolutional neural networks

Zhou, Wen; Jia, Jinyuan; Huang, Chengxi; Cheng, Yongqing

Abstract

With the rapid development of Web3D technologies, sketch-based model retrieval has become an increasingly important challenge, while the application of Virtual Reality and 3D technologies has made shape retrieval of furniture over a web browser feasible. In this paper, we propose a learning framework for shape retrieval based on two Siamese VGG-16 Convolutional Neural Networks (CNNs), and a CNN-based hybrid learning algorithm to select the best view for a shape. In this algorithm, the AlexNet and VGG-16 CNN architectures are used to perform classification tasks and to extract features, respectively. In addition, a feature fusion method is used to measure the similarity relation of the output features from the two Siamese networks. The proposed framework can provide new alternatives for furniture retrieval in the Web3D environment. The primary innovation is in the employment of deep learning methods to solve the challenge of obtaining the best view of 3D furniture, and to address cross-domain feature learning problems. We conduct an experiment to verify the feasibility of the framework and the results show our approach to be superior in comparison to many mainstream state-of-the-art approaches.

Citation

Zhou, W., Jia, J., Huang, C., & Cheng, Y. (2020). Web3D learning framework for 3D shape retrieval based on hybrid convolutional neural networks. Tsinghua Science and Technology, 25(1), 93-102. https://doi.org/10.26599/TST.2018.9010113

• Journal Article Type: Article
• Acceptance Date: Jul 20, 2018
• Online Publication Date: Jul 22, 2019
• Publication Date: 2020-02
• Deposit Date: Aug 9, 2019
• Publicly Available Date: Aug 9, 2019
• Journal: Tsinghua Science and Technology
• Print ISSN: 1007-0214
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 25
• Issue: 1
• Pages: 93-102
• DOI: https://doi.org/10.26599/TST.2018.9010113
• Keywords: Multidisciplinary
• Public URL: https://hull-repository.worktribe.com/output/2332873
• Publisher URL: https://ieeexplore.ieee.org/document/8768209