3D Model Classification Based on Shannon Entropy Representative Feature and Voting Mechanism
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摘要: 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。Abstract: 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.
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表 1 不同视图数目下的分类准确率(%)
网络模型 投票算法 3V 6V 9V 12V 18V RegNet HV 94.05 93.83 93.98 94.82 94.27 SV 94.05 94.60 94.27 94.71 94.60 
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表 2 不同RegNet网络的分类准确率(%)
RegNet模型 stage1 stage2 stage3 stage4 flops(B) params(M) HV SV RegNet2X 1×block 1×block 4×block 7×block 0.2 2.7 92.51 93.83 RegNet4X 1×block 2×block 7×block 12×block 0.4 5.2 93.50 94.16 RegNet6X 1×block 3×block 5×block 7×block 0.6 6.2 93.83 94.60 RegNet8X 1×block 3×block 7×block 5×block 0.8 7.3 94.16 94.71
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表 3 ECA不同嵌入位置对RegNet分类的影响(%)
投票算法 ECA1 ECA2 ECA3 ECA4 HV 93.72 93.72 93.94 94.05 SV 94.60 94.49 95.26 94.60
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表 4 ECA对RegNet分类的影响(%)
网络模型 投票算法 视角1 视角2 视角3 视角4 视角5 视角6 RegNet HV 92.51 92.95 93.06 93.83 94.05 88.66 SV 93.06 93.94 94.05 94.60 94.49 89.43 RegNet+ECA HV 93.28 94.27 93.39 93.94 94.05 88.88 SV 93.72 94.71 94.93 95.26 95.04 90.42
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表 5 多视角代表性特征的对比(%)
类别 视角1 视角2 视角3 视角4 视角5 视角6 R-SV R-HV bathtub 92.00 98.00 98.00 100.00 96.00 88.00 100.00 100.00 bed 99.00 100.00 100.00 100.00 100.00 97.00 100.00 100.00 chair 100.00 100.00 100.00 100.00 100.00 97.00 100.00 100.00 desk 91.86 88.37 89.53 86.05 91.86 75.58 91.86 91.86 dresser 83.72 91.86 95.35 94.19 89.53 87.21 94.19 94.19 monitor 100 99.00 100.00 100.00 99.00 99.00 100.00 100.00 night_stand 89.53 82.56 79.07 82.56 88.37 72.09 83.72 86.05 sofa 99.00 98.00 98.00 99.00 98.00 96.00 99.00 99.00 table 92.00 91.00 85.00 87.00 88.00 87.00 92.00 93.00 toilet 96.00 99.00 100.00 100.00 100.00 100.00 100.00 100.00 平均准确率 94.71 94.93 94.60 94.93 95.26 90.53 96.15 96.48
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