---
author:
- 'Haoyin Zhou, Tao Zhang,  and Jayender Jagadeesan,  [^1]'
bibliography:
- 'IEEEabrv.bib'
- 'bare\_jrnl.bib'
title: |
    Re-weighting and 1-Point RANSAC-Based\
    P$n$P Solution to Handle Outliers
---

[Shell : Bare Demo of IEEEtran.cls for Computer Society Journals]{}

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

perspective-$n$-point (P$n$P) problem aims to determine the position and orientation of a calibrated camera from $n$ known correspondences between three-dimensional (3D) object points and their two-dimensional (2D) image projections. P$n$P is a core problem in the computer vision field and has found many applications, such as robot vision navigation [@mur2015orb], augmented reality [@muller2013mobile], and computer animation. In the past decades, many effective P$n$P approaches have been proposed with very fast computational speed [@ferraz2014very][@lepetit2009epnp] and high accuracy [@zheng2013revisiting][@lu2000fast].

To date, most P$n$P algorithms are designed under the assumption that no outlier exists among the given 3D-2D correspondences. However, in practical applications, this outlier-free assumption is often difficult to satisfy. This is because image feature detection and matching approaches, such as SURF [@bay2006surf], BRISK [@leutenegger2011brisk] and ORB [@rublee2011orb], do not always give perfect results due to scaling, illumination, shadow and occlusion. Outliers are often unavoidable and they have a significant impact on the P$n$P methods. Even a small percentage of outliers will 