P1AC: Revisiting Absolute Pose From a Single Affine Correspondence

TL;DR

This paper introduces P1AC, a novel minimal solver for absolute camera pose estimation from a single affine correspondence, outperforming traditional methods like P3P in accuracy and efficiency.

Contribution

The paper presents the first general solution for absolute pose estimation using a single affine correspondence, removing prior restrictive assumptions and enhancing large-scale localization.

Findings

P1AC is numerically stable under various noise conditions.

P1AC achieves higher accuracy than P3P on localization benchmarks.

The method reduces computational complexity by requiring only one correspondence.

Abstract

Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://github.com/jonathanventura/P1AC/ .