Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (5): 222-231.DOI: 10.3778/j.issn.1002-8331.2111-0315

• Graphics and Image Processing • Previous Articles     Next Articles

Deep Ensemble Learning for Diversified 3D Model Classification

BAI Shaojin, BAI Jing, SI Qinglong, JI Hui, YUAN Tao   

  1. 1.School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China
    2.The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
  • Online:2023-03-01 Published:2023-03-01



  1. 1.北方民族大学 计算机科学与工程学院,银川 750021
    2.国家民委图形图像智能处理实验室,银川 750021

Abstract: The current 3D model classification methods based on deep learning are mostly worked on specific tasks. Considering that these methods do not perform well and lack of universality in the diversified 3D model classification tasks, it proposes a general end-to-end deep ensemble learning E2E-DEL, which consists of a set of several base learners and an ensemble learner so as to automatically learn composite features of complex 3D models. And it uses a hierarchical iterative learning strategy to comprehensively consider the feature learning ability of different levels of networks and balance the learning effect of base learners and the ensemble learner. So the model can be adaptive in the diversified 3D model classification tasks. Based on this, it proposes a multi-view deep ensemble learning:MV-DEL, which can be used in general, fine-grained, and zero-shot 3D model classification tasks. Experiments on several public datasets show that the proposed method has good generalization and universality.

Key words: deep learning, deep ensemble learning, 3D model classification

摘要: 基于深度学习的三维模型分类方法大都面向特定的具体任务,在面向三维模型多样化分类任务时表现不佳,泛用性不足。为此,提出了一种通用的端到端的深度集成学习模型E2E-DEL(end-to-end deep ensemble learning),由多个初级学习器和一个集成学习器组成,可以自动学习复杂三维模型的复合特征信息;并使用层次迭代式学习策略,综合考量不同层次网络的特征学习能力,合理平衡各个初级学习器的子特征学习和集成学习器的集成特征学习效果,自适应于三维模型多样化分类任务。基于此,设计了一种面向多视图的深度集成学习网络MV-DEL(multi-view deep ensemble learning),应用于一般性、细粒度、零样本三种不同类型的三维模型分类任务中。在多个公开数据集上的实验验证了该方法具有良好的泛化性与普适性。

关键词: 深度学习, 深度集成学习, 三维模型分类