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ReliefNet: Fast Bas-relief Generation from 3D Scenes
Computer-Aided Design ( IF 3.0 ) Pub Date : 2020-08-21 , DOI: 10.1016/j.cad.2020.102928
Zhongping Ji , Wei Feng , Xianfang Sun , Feiwei Qin , Yigang Wang , Yu-Wei Zhang , Weiyin Ma

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.



中文翻译:

ReliefNet:从3D场景快速生成浅浮雕

大多数先前的浅浮雕生成方法运行缓慢,或者需要调整几个重要参数。这些问题严重降低了浅浮雕建模的效率。我们介绍了一种基于深度学习技术的3D对象中高质量浮雕的快速生成方法。与用于图像任务的神经网络不同,拟议的浮雕网络(ReliefNet)经过精心设计,可以处理图形领域的建模问题。我们设计ReliefNet并为其配备特殊的损耗函数,以使网络可以解决浅浮雕建模的基本问题。我们的网络消除了高度差,并同时保留了丰富的细节。与以前的方法相比,优点是我们的方法不需要参数调整并且非常有效。培训ReliefNet之后,网络的一次前馈传递可以立即产生浅浮雕。为了证明我们方法的有效性和有效性,我们在高分辨率的3D场景范围内进行了广泛的实验,并与最新方法进行了比较。

更新日期:2020-08-26
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