HARA: A Hierarchical Approach for Robust Rotation Averaging

TL;DR

HARA introduces a hierarchical method for rotation averaging that builds a robust initial solution by prioritizing triplet support, effectively filtering outliers and improving accuracy in 3D rotation estimation.

Contribution

The paper presents a novel hierarchical approach for rotation averaging that enhances robustness and outlier filtering by incrementally constructing the rotation graph based on triplet support.

Findings

Achieves state-of-the-art results on synthetic datasets.

Demonstrates robustness in real-world datasets.

Effectively filters outliers before nonlinear optimization.

Abstract

We propose a novel hierarchical approach for multiple rotation averaging, dubbed HARA. Our method incrementally initializes the rotation graph based on a hierarchy of triplet support. The key idea is to build a spanning tree by prioritizing the edges with many strong triplet supports and gradually adding those with weaker and fewer supports. This reduces the risk of adding outliers in the spanning tree. As a result, we obtain a robust initial solution that enables us to filter outliers prior to nonlinear optimization. With minimal modification, our approach can also integrate the knowledge of the number of valid 2D-2D correspondences. We perform extensive evaluations on both synthetic and real datasets, demonstrating state-of-the-art results.