Image signal processors (ISPs) are historically grown legacy software systems
for reconstructing color images from noisy raw sensor measurements. Each
smartphone manufacturer has developed its ISPs with its own characteristic
heuristics for improving the color rendition, for example, skin tones and other
visually essential colors. The recent interest in replacing the historically
grown ISP systems with deep-learned pipelines to match DSLR's image quality
improves structural features in the image. However, these works ignore the
superior color processing based on semantic scene analysis that distinguishes
mobile phone ISPs from DSLRs. Here, we present MetaISP, a single model designed
to learn how to translate between the color and local contrast characteristics
of different devices. MetaISP takes the RAW image from device A as input and
translates it to RGB images that inherit the appearance characteristics of
devices A, B, and C. We achieve this result by employing a lightweight deep
learning technique that conditions its output appearance based on the device of
interest. In this approach, we leverage novel attention mechanisms inspired by
cross-covariance to learn global scene semantics. Additionally, we use the
metadata that typically accompanies RAW images and estimate scene illuminants
when they are unavailable.

MetaISP 是一个单一模型，旨在学习如何在不同设备间转换颜色和局部对比特征，通过轻量级深度学习技术基于设备的相关特征生成 RGB 图片，并利用受交叉协方差启发的新型注意力机制学习全局场景语义，并在场景光源不可用时利用伴随原始图像的元数据进行估计。

MetaISP -- 利用全球场景结构进行准确的多设备色彩还原

MetaISP -- Exploiting Global Scene Structure for Accurate Multi-Device  Color Rendition

We present an approach to semantic scene analysis using deep convolutional
networks. Our approach is based on tangent convolutions - a new construction
for convolutional networks on 3D data. In contrast to volumetric approaches,
our method operates directly on surface geometry. Crucially, the construction
is applicable to unstructured point clouds and other noisy real-world data. We
show that tangent convolutions can be evaluated efficiently on large-scale
point clouds with millions of points. Using tangent convolutions, we design a
deep fully-convolutional network for semantic segmentation of 3D point clouds,
and apply it to challenging real-world datasets of indoor and outdoor 3D
environments. Experimental results show that the presented approach outperforms
other recent deep network constructions in detailed analysis of large 3D
scenes.

使用新型卷积网络（tangent convolutions）进行语义场景分析，在室内和室外 3D 环境中的实验结果表明，该方法比其他深度网络构成在大型 3D 场景的详细分析中表现更好。