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Three-dimensional shape reconstruction of objects from a single depth view using deep U-Net convolutional neural network with bottle-neck skip connections

Abstract:

Three-dimensional (3D) shape reconstruction of objects requires multiple scans and complex reconstruction algorithms. An alternative approach is to infer the 3D shape of an object from a single depth image (i.e. single depth view). This study presents such a 3D shape reconstructor based on U-Net 3D-convolutional neural network (3D-CNN) with bottle-neck skipped connection blocks (U-Net BNSC 3D-CNN) to infer the 3D shapes of objects from only a single depth view. The BNSC block is a fully convolutional block that uses skip connections to improve the performance of the sequential 3D-convolutional layers of U-Net. The primary advantage of U-Net BNSC 3D-CNN is improving the accuracy of shape reconstruction while reducing the computational load. The evaluation of the proposed U-Net BNSC 3D-CNN uses unseen views from trained and untrained objects with two public databases, ShapeNet and Grasp database. Our reconstructor achieves 72.17% and 69.97% accuracy in terms of the Jaccard similarity index for trained and untrained objects, respectively, with the ShapeNet database, whereas previous reconstructor based on 3D-CNN achieves 66.43% and 58.35%. With Grasp database, our reconstructor achieves 87.03% and 85.35%, whereas 3D-CNN 76.52% and 76.02%. Also, our U-Net BNSC 3D-CNN reduces the computational load of the standard 3D-CNN reconstructor by 6.67% in the computation time and by 98.69% in the number of trainable parameters.