# Efficient Subpixel Refinement with Symbolic Linear Predictors

arXiv: https://arxiv.org/abs/1804.10750 · HTML: https://mlsanity.com/paper/1804.10750/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [XRefine: Attention-Guided Keypoint Match Refinement](https://mlsanity.com/paper/2601.12530/) (2026)

## Abstract (arXiv)

We present an efficient subpixel refinement method usinga learning-based approach called Linear Predictors. Two key ideas are shown in this paper. Firstly, we present a novel technique, called Symbolic Linear Predictors, which makes the learning step efficient for subpixel refinement. This makes our approach feasible for online applications without compromising accuracy, while taking advantage of the run-time efficiency of learning based approaches. Secondly, we show how Linear Predictors can be used to predict the expected alignment error, allowing us to use only the best keypoints in resource constrained applications. We show the efficiency and accuracy of our method through extensive experiments.