Accelerating Outlier-robust Rotation Estimation by Stereographic Projection

TL;DR

This paper introduces a fast, robust rotation estimation method using stereographic projection and spatial voting, capable of handling large, noisy datasets with high outlier rates efficiently.

Contribution

It proposes a novel geometric and projection-based approach for rotation estimation that is significantly faster and more robust than existing methods, especially for large-scale, outlier-rich data.

Findings

Achieves rotation estimation within 0.07 seconds for 1 million points.

Maintains an angular error of only 0.01 degrees.

Outperforms existing methods in accuracy and efficiency.

Abstract

Rotation estimation plays a fundamental role in many computer vision and robot tasks. However, efficiently estimating rotation in large inputs containing numerous outliers (i.e., mismatches) and noise is a recognized challenge. Many robust rotation estimation methods have been designed to address this challenge. Unfortunately, existing methods are often inapplicable due to their long computation time and the risk of local optima. In this paper, we propose an efficient and robust rotation estimation method. Specifically, our method first investigates geometric constraints involving only the rotation axis. Then, it uses stereographic projection and spatial voting techniques to identify the rotation axis and angle. Furthermore, our method efficiently obtains the optimal rotation estimation and can estimate multiple rotations simultaneously. To verify the feasibility of our method, we conduct comparative experiments using both synthetic and real-world data. The results show that, with GPU assistance, our method can solve large-scale ($10^6$ points) and severely corrupted (90\% outlier rate) rotation estimation problems within 0.07 seconds, with an angular error of only 0.01 degrees, which is superior to existing methods in terms of accuracy and efficiency.