Deep Learning-Based 3D Shape Feature Extraction on Flash Animation Style

Abstract

Flash animation, as a kind of digital learning resource, is an important media for delivering information content, and more importantly, it is an important online learning resource with text, graphics, images, audio, video, interaction, dynamic effects, etc. Flash animation, with its powerful multimedia interaction and presentation capabilities, is widely used in distance education, high-quality course websites, Q&A platforms, etc. With the continuous development of deep learning, the 3D shape feature extraction method combined with deep learning has become a hot research topic. In this paper, we combine deep learning with traditional 3D shape feature extraction methods, so that we can not only break the bottleneck of nondeep learning methods but also improve the accuracy of 3D shape data classification and retrieval tasks, especially in the case of non-rigid 3D shapes. The scheme in this paper not only does not require a large number of training samples but also its feature extraction for flash animation is accurate. Experiments show that the success rate of accurate feature extraction of this paper's scheme is higher than that of the state-of-the-art methods.