ReliefNet: Fast Bas-relief Generation from 3D Scenes

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 basreliefs 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. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper introduces a fast bas-relief generation method based on deep learning, which eliminates the need for parameter tuning and maintains rich details. The proposed ReliefNet is designed to solve the essential problem of bas-relief modeling effectively, showcasing its performance through extensive experiments on various high-resolution 3D scenes.