In this study, we present a new and innovative framework for acquiring high-quality SVBRDF maps. Our approach addresses the limitations of the current methods and proposes a new solution. The core of our method is a simple hardware setup consisting of a consumer-level camera, LED lights, and a carefully designed network that can accurately obtain the high-quality SVBRDF properties of a nearly planar object. By capturing a flexible number of images of an object, our network uses different subnetworks to train different property maps and employs appropriate loss functions for each of them. To further enhance the quality of the maps, we improved the network structure by adding a novel skip connection that connects the encoder and decoder with global features. Through extensive experimentation using both synthetic and real-world materials, our results demonstrate that our method outperforms previous methods and produces superior results. Furthermore, our proposed setup can also be used to acquire physically based rendering maps of special materials.

在这项研究中，我们提出了一个用于获取高质量 SVBRDF 地图的新的创新框架。我们的方法解决了当前方法的局限性并提出了一种新的解决方案。我们方法的核心是一个简单的硬件设置，由消费级相机、LED 灯和精心设计的网络组成，可以准确获得近平面物体的高质量 SVBRDF 属性。通过捕获对象的灵活数量的图像，我们的网络使用不同的子网络来训练不同的属性图，并为每个属性图采用适当的损失函数。为了进一步提高地图的质量，我们通过添加一种新颖的跳跃连接来改进网络结构，该跳跃连接将编码器和解码器与全局特征连接起来。通过使用合成材料和真实材料进行的广泛实验，我们的结果表明我们的方法优于以前的方法并产生了优异的结果。此外，我们提出的设置还可用于获取特殊材料的基于物理的渲染图。