ReliefNet : Fast Bas-relief Generation from 3D Scenes

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
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Author(s)

  • Zhongping Ji
  • Wei Feng
  • Xianfang Sun
  • Feiwei Qin
  • Yigang Wang
  • Yu-Wei Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number102928
Journal / PublicationCAD Computer Aided Design
Volume130
Online published21 Aug 2020
Publication statusPublished - Jan 2021

Abstract

Most previous methods of bas-relief generation run slow, or require tuning several important parameters. These issues seriously reduce the efficiency of bas-relief modeling. We introduce a fast generation method for high-quality bas-reliefs from 3D objects based on a deep learning technique. Unlike neural networks for image tasks, the proposed network for reliefs (ReliefNet) is elaborately designed to deal with a modeling problem in the field of graphics. We design our ReliefNet and equip it with a special loss function with the aim that the network can solve the essential problem of bas-relief modeling. Our network eliminates the height gaps and maintains the rich details simultaneously. The advantage over previous methods is that our method does not require parameter tuning and is a very efficient. Once the ReliefNet has been trained, a bas-relief can be produced by one feed-forward pass of the network instantly. To demonstrate the performance and effectiveness of our method, extensive experiments on a range of 3D scenes with high resolutions and comparisons to state-of-the-art methods are conducted.

Research Area(s)

  • 3D scene, Bas-relief, Height field, Relief modeling, ReliefNet

Citation Format(s)

ReliefNet : Fast Bas-relief Generation from 3D Scenes. / Ji, Zhongping; Feng, Wei; Sun, Xianfang; Qin, Feiwei; Wang, Yigang; Zhang, Yu-Wei; Ma, Weiyin.

In: CAD Computer Aided Design, Vol. 130, 102928, 01.2021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review