Incremental Rotation Averaging Revisited

TL;DR

This paper introduces IRAv4, an improved incremental rotation averaging method that uses a task-specific connected dominating set for more accurate rotation alignment, validated on the 1DSfM dataset.

Contribution

The paper proposes a novel IRAv4 method with a task-specific connected dominating set for enhanced rotation averaging accuracy and robustness.

Findings

IRAv4 outperforms previous methods on 1DSfM dataset

The connected dominating set improves alignment reliability

The proposed pipeline enhances rotation estimation accuracy

Abstract

In order to further advance the accuracy and robustness of the incremental parameter estimation-based rotation averaging methods, in this paper, a new member of the Incremental Rotation Averaging (IRA) family is introduced, which is termed as IRAv4. As its most significant feature, a task-specific connected dominating set is extracted in IRAv4 to serve as a more reliable and accurate reference for rotation local-to-global alignment. This alignment reference is incrementally constructed, together with the absolute rotations of the vertices belong to it simultaneously estimated. Comprehensive evaluations are performed on the 1DSfM dataset, by which the effectiveness of both the reference construction method and the entire rotation averaging pipeline proposed in this paper is demonstrated.