The Fast Bilateral Solver

Abstract

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.

Cited by in corpus (5)

• PixelNet: Towards a General Pixel-level Architecture
• Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation
• Simple Does It: Weakly Supervised Instance and Semantic Segmentation
• Superpixel Convolutional Networks using Bilateral Inceptions
• The bilateral solver for quality estimation based multi-focus image fusion