3D lookup tables (3D LUTs) are a key component for image enhancement. Modern
image signal processors (ISPs) have dedicated support for these as part of the
camera rendering pipeline. Cameras typically provide multiple options for
picture styles, where each style is usually obtained by applying a unique
handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are
notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is
required. For this reason and other implementation limitations, their use on
mobile devices is less popular. In this work, we propose a Neural Implicit LUT
(NILUT), an implicitly defined continuous 3D color transformation parameterized
by a neural network. We show that NILUTs are capable of accurately emulating
real 3D LUTs. Moreover, a NILUT can be extended to incorporate multiple styles
into a single network with the ability to blend styles implicitly. Our novel
approach is memory-efficient, controllable and can complement previous methods,
including learned ISPs. Code, models and dataset available at:
this https URL

这项工作提出了一种神经隐式 LUT（NILUT），作为相机渲染管道的一部分，用于快速而低成本地进行多种图片风格的连续 3D 颜色转换，较传统的 3D LUT 方法更具有内存效率。