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.

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。