论文标题
有效的反射率捕获具有深厚的Experts
Efficient Reflectance Capture with a Deep Gated Mixture-of-Experts
论文作者
论文摘要
我们提出了一个新颖的框架，以使用深层的杂音混合物，以无像素独立的方式有效地获取近平面各向异性反射率。尽管现有工作采用统一网络来处理所有可能的输入，但我们的网络会自动学习在输入上条件以进行增强重建。我们根据光度测量值，基本上是用于质量的一般交易一般性，训练一个门控模块，以选择许多专业解码器进行反射重建。每个解码器也附加了一个常见的预训练潜在变换模块，以抵消解码器数量增加的负担。另外，可以共同优化采集过程中的照明条件。我们的框架的有效性在使用近场光阶段对各种具有挑战性的样品进行了验证。与最新技术相比，我们的结果在相同的输入带宽下得到改进，并且我们的带宽可以减少到约1/3，以获得相等的质量结果。
We present a novel framework to efficiently acquire near-planar anisotropic reflectance in a pixel-independent fashion, using a deep gated mixtureof-experts. While existing work employs a unified network to handle all possible input, our network automatically learns to condition on the input for enhanced reconstruction. We train a gating module to select one out of a number of specialized decoders for reflectance reconstruction, based on photometric measurements, essentially trading generality for quality. A common, pre-trained latent transform module is also appended to each decoder, to offset the burden of the increased number of decoders. In addition, the illumination conditions during acquisition can be jointly optimized. The effectiveness of our framework is validated on a wide variety of challenging samples using a near-field lightstage. Compared with the state-of-the-art technique, our results are improved at the same input bandwidth, and our bandwidth can be reduced to about 1/3 for equal-quality results.
