---
abstract: 'We present an efficient subpixel refinement method using a 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.'
author:
- |
    Vincent Lui Jonathon Geeves Winston Yii Tom Drummond\
    Department of Electrical and Computer Systems Engineering\
    Monash University, Victoria, Australia\
    [.\[lastname\]@monash.edu]{}
bibliography:
- 'egbib.bib'
title: Efficient Subpixel Refinement with Symbolic Linear Predictors
---

Introduction
============

A small but important step for accurate 3D reconstructions in robotic applications such as visual SLAM is subpixel refinement. One of the first steps in a conventional visual SLAM pipeline involves the extraction of salient keypoints, followed by the formation of point correspondences. As keypoint extraction methods [@Harris88; @Rosten2006] are usually accurate up to only one pixel, the goal 