  We present the bilateral solver, a novel algorithm for edge-aware smoothing
that combines the flexibility and speed of simple filtering approaches with the
accuracy of domain-specific optimization algorithms. Our technique is capable
of matching or improving upon state-of-the-art results on several different
computer vision tasks (stereo, depth superresolution, colorization, and
semantic segmentation) while being 10-1000 times faster than competing
approaches. The bilateral solver is fast, robust, straightforward to generalize
to new domains, and simple to integrate into deep learning pipelines.
