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基于香农熵代表性特征和投票机制的三维模型分类

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目前基于视图的3维模型分类方法存在单视图视觉信息不充分、多视图信息冗余的问题,且同等对待所有视图会忽略不同投影视角之间的差异性.针对上述问题,该文提出一种基于香农熵代表性特征和投票机制的3维模型分类方法.首先,通过在3维模型周围均匀设置多个视角组来获取表征模型的多组视图集.为了有效提取视图深层特征,在特征提取网络中引入通道注意力机制;然后,针对Softmax函数输出的视图判别性特征,使用香农熵来选择代表性特征,从而避免多视图特征冗余;最后,基于多个视角组的代表性特征利用投票机制来完成3维模型分类.实验表明:该方法在3维模型数据集ModelNet10上的分类准确率达到96.48％,分类性能突出.
3D Model Classification Based on Shannon Entropy Representative Feature and Voting Mechanism
At present, view-based 3D model classification has the problems of insufficient visual information for single view and redundant information for multiple views, and treating all views equally will ignore the differences between different projection angles. To solve the above problems, a 3D model classification method based on Shannon entropy representative feature and voting mechanism is proposed. Firstly, multiple angle groups are set uniformly around 3D model, and multiple view sets representing the model are obtained. In order to extract effectively deep features from view, channel attention mechanism is introduced into the feature extraction network. Secondly, based on view discriminative features output from Softmax function, Shannon entropy is used to select representative feature for avoiding redundant feature of multiple views. Finally, based on representative features from multiple angle groups, voting mechanism is used to classify 3D model. Experiments show that the classification accuracy of the proposed method on 3D model dataset ModelNet10 reaches 96.48％, and classification performance is outstanding.

3D model classificationAttention mechanismShannon entropy representative featureVoting mechanism

高雪瑶、闫少康、张春祥

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哈尔滨理工大学计算机科学与技术学院 哈尔滨 150080

3维模型分类 注意力机制 香农熵代表性特征 投票机制

国家自然科学基金国家自然科学基金中国博士后科学基金黑龙江省自然科学基金黑龙江省自然科学基金黑龙江省自然科学基金黑龙江省自然科学基金

61502124609030822014M560249LH2022F031LH2022F030F2015041F201420

2024

10.11999/JEIT230405

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子：1.302
ISSN：1009-5896
年,卷(期)：2024.46(4)