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.