  The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image
enhancement tasks, which models a non-linear 3D color transform by sparsely
sampling it into a discretized 3D lattice. Previous works have made efforts to
learn image-adaptive output color values of LUTs for flexible enhancement but
neglect the importance of sampling strategy. They adopt a sub-optimal uniform
sampling point allocation, limiting the expressiveness of the learned LUTs
since the (tri-)linear interpolation between uniform sampling points in the LUT
transform might fail to model local non-linearities of the color transform.
Focusing on this problem, we present AdaInt (Adaptive Intervals Learning), a
novel mechanism to achieve a more flexible sampling point allocation by
adaptively learning the non-uniform sampling intervals in the 3D color space.
In this way, a 3D LUT can increase its capability by conducting dense sampling
in color ranges requiring highly non-linear transforms and sparse sampling for
near-linear transforms. The proposed AdaInt could be implemented as a compact
and efficient plug-and-play module for a 3D LUT-based method. To enable the
end-to-end learning of AdaInt, we design a novel differentiable operator called
AiLUT-Transform (Adaptive Interval LUT Transform) to locate input colors in the
non-uniform 3D LUT and provide gradients to the sampling intervals. Experiments
demonstrate that methods equipped with AdaInt can achieve state-of-the-art
performance on two public benchmark datasets with a negligible overhead
increase. Our source code is available at https://github.com/ImCharlesY/AdaInt.
