基于特征融合的三维人脸点云质量判断

针对使用双目结构光扫描仪获取的三维人脸点云,提出了一种特征融合网络(FFN)来完成人脸点云质量判断任务.首先,对三维点云预处理切割出人脸面部区域,使用点云和对应的二维平面投影得到的图像作为输入;其次,分别训练用于点云学习的动态图卷积神经网络(DGCNN)和ShuffleNet两个模块;然后,提取出两个网络模块的中间层特...

Quality judgment of 3D face point cloud based on feature fusion

A Feature Fusion Network （FFN） was proposed to judge the quality of 3D face point cloud acquired by binocular structured light scanner. Firstly， the 3D point cloud was preprocessed to cut out the face area， and the image obtained from the point cloud and the corresponding 2D plane projection was used as the input. Secondly， Dynamic Graph Convolutional Neural Network （DGCNN） and ShuffleNet were trained for point cloud learning. Then， the middle layer features of the two network modules were extracted and fused to fine-tune the whole network. Finally， three full connected layers were used to realize the five-class classification of 3D face point cloud （excellent， ordinary， stripe， burr， deformation）. The proposed FFN achieved the classification accuracy of 83.7%， which was 5.8% higher than that of ShufflNet and 2.2% higher than that of DGCNN. The experimental results show that the weighted fusion of two-dimensional image features and point cloud features can achieve the complementary effect between different features.