Reformed Residual Network With Sparse Feedbacks for 3D Reconstruction From a Single Image | Zendy

Yujuan Sun | Zendy; Muwei Jian | Zendy; Xiaofeng Zhang | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Reformed Residual Network With Sparse Feedbacks for 3D Reconstruction From a Single Image

Author(s) -

Yujuan Sun,

Muwei Jian,

Xiaofeng Zhang

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2880494

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Deep neural networks are difficult to train due to the large number of unknown parameters. To increase the trainable performance, we present a novel network model with moderate depth for three-dimensional reconstruction. The proposed network model, called SFResNet, only has eight layers, and sparse feedbacks were added in the middle and last layers, which is mainly used to add the constraints and improve the stability of the network model. In addition, a joint strategy is proposed to reduce the artificial Mosaic trace at the seam of the patches; hence, SFResNet can also evaluate an input image of any size. Visually pleasing output results can be produced with a reconstructed shape and normal surface. The experimental results show the effectiveness of the proposed method.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research