# The Domain Transform Solver

arXiv: https://arxiv.org/abs/1805.04590 · HTML: https://mlsanity.com/paper/1805.04590/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2018.

## Built on by (most cited first, 2 of 2)

- [Deterministic Guided LiDAR Depth Map Completion](https://mlsanity.com/paper/2106.07256/) (2021)
- [Towards High-Frequency Tracking and Fast Edge-Aware Optimization](https://mlsanity.com/paper/2309.00777/) (2023)

## Abstract (arXiv)

We present a framework for edge-aware optimization that is an order of magnitude faster than the state of the art while having comparable performance. Our key insight is that the optimization can be formulated by leveraging properties of the domain transform, a method for edge-aware filtering that defines a distance-preserving 1D mapping of the input space. This enables our method to improve performance for a variety of problems including stereo, depth super-resolution, and render from defocus, while keeping the computational complexity linear in the number of pixels. Our method is highly parallelizable and adaptable, and it has demonstrable scalability with respect to image resolution.