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 …

Año de publicación:

2021

Keywords:

Fuente:

google