多视图融合的三维模型分类

Multi-view Fusion 3D Model Classification

目前基于视图的三维模型分类已经成为一个研究热点。但是,现有的方法会产生大量冗余视图,且所有的视图都被平等对待,忽略了不同视图之间的差异性和重要性。针对以上问题,提出了多视图融合的三维模型分类方法。该方法首先使用加入混合域注意力机制的视图特征提取网络提取视图特征,然后对这些视图特征进行特征融合,将融合后的特征输入到加入通道域注意力机制的视图权重学习网络,根据不同视图对三维模型重要性不同赋予不同权重,形成具有代表性的特征描述符用于三维模型分类。实验结果表明,在刚性三维模型数据集ModelNet10和ModelNet40中分类准确率分别达到了98.3%和95.5%。

At present,view-based 3D model classification is a research hotspot.However,current methods produce many redundant views,and all views are treated equally,ignoring their differences and importance.To solve the above problems,we propose a multi-view fusion 3D model classification method.This method first extracts view features using the view feature extraction network with mixed domain attention,and then fuses these view features and inputs the fused features into the view weight learning network with channel attention,giving different weights to different views according to their importance to the 3D model,and forming representative feature descriptors for 3D model classification.Experimental results shows that the classification accuracy rates in the rigid 3D model data sets ModelNet10 and ModelNet40 reached 98.3%and 95.5%.