基于c -双注意网络的多视点三维图像重建

T. U. Kamble, S. Mahajan
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引用次数: 1

摘要

基于多视点成像的三维图像重建在建筑、灾害管理、城市规划等多个应用领域得到了广泛的应用。多视点图像的三维重建由于自由度高、重建精度不高,仍然是一个挑战。本研究引入了用于三维图像重建的混合深度学习技术,其中提出了C-dual注意层来生成特征映射以支持图像重建。所提出的三维图像重建使用了用于重建的编码器-解码器-细化器。最初,这些特征是自动从AlexNet和ResNet-50特征中提取的。然后,利用所提出的c -双注意层生成特征之间的通道间和空间间关系,以提高重建精度。使用信道注意层评估信道间关系,使用编码器模块的空间注意层评估空间间关系。在这里,将空间注意层生成的特征组合在一起,形成二维地图中的特征图。提出的c -双注意力编码器提供了增强的功能,有助于获得增强的3D图像重建。基于loss、IoU_3D和IoU_2D对该方法进行了评价,得到的结果分别为0.0721、1.25和1.37。
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3D Image reconstruction using C-dual attention network from multi-view images
3D image reconstruction using multi-view imaging is widely utilized in several application domains: construction field, disaster management, urban planning, etc. The 3D reconstruction from the multi-view image is still challenging due to the high freedom and inaccurate reconstruction. This research introduces the hybrid deep learning technique for reconstructing the 3D image, in which the C-dual attention layer is proposed for generating the feature map to support the image reconstruction. The proposed 3D image reconstruction uses the encoder–decoder–refiner which is utilized for reconstruction. Initially, the features are extracted from the AlexNet and ResNet-50 features automatically. Then, the proposed C-dual attention layer is utilized for generating the inter-channel and inter-spatial relationship among the features to obtain enhanced reconstruction accuracy. The inter-channel relationship is evaluated using the channel attention layer, and the inter-spatial relationship is evaluated using the spatial attention layer of the encoder module. Here, the features generated by the spatial attention layer are combined to form the feature map in a 2D map. The proposed C-dual attention encoder provides enhanced features that help to acquire enhanced 3D image reconstruction. The proposed method is evaluated based on loss, IoU_3D, and IoU_2D, and acquired the values of 0.0721, 1.25 and 1.37, respectively.
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