Performance Analysis and Robustification of Single-query 6-DoF Camera Pose Estimation

TL;DR

This paper systematically compares three 6-DoF camera pose estimation methods, proposes a hybrid approach combining feature-based and mutual-information strategies, and demonstrates its superior robustness and accuracy across various environmental conditions.

Contribution

It introduces a hybrid pose estimation method that leverages the strengths of feature-based and mutual-information approaches, improving robustness and accuracy.

Findings

Hybrid approach outperforms individual methods in large environmental variance.

Hybrid matches the accuracy of feature-based when conditions are similar.

Feature-based approach is more accurate with at least 4 matching points.

Abstract

We consider a single-query 6-DoF camera pose estimation with reference images and a point cloud, i.e. the problem of estimating the position and orientation of a camera by using reference images and a point cloud. In this work, we perform a systematic comparison of three state-of-the-art strategies for 6-DoF camera pose estimation, i.e. feature-based, photometric-based and mutual-information-based approaches. The performance of the studied methods is evaluated on two standard datasets in terms of success rate, translation error and max orientation error. Building on the results analysis, we propose a hybrid approach that combines feature-based and mutual-information-based pose estimation methods since it provides complementary properties for pose estimation. Experiments show that (1) in cases with large environmental variance, the hybrid approach outperforms feature-based and mutual-information-based approaches by an average of 25.1% and 5.8% in terms of success rate, respectively; (2) in cases where query and reference images are captured at similar imaging conditions, the hybrid approach performs similarly as the feature-based approach, but outperforms both photometric-based and mutual-information-based approaches with a clear margin; (3) the feature-based approach is consistently more accurate than mutual-information-based and photometric-based approaches when at least 4 consistent matching points are found between the query and reference images.