RAGO: Recurrent Graph Optimizer For Multiple Rotation Averaging

TL;DR

RAGO is a real-time deep recurrent graph optimizer designed for multiple rotation averaging, effectively handling noisy measurements and gauge freedom issues to improve accuracy in camera rotation estimation.

Contribution

It introduces a learnable, gauge-invariant iterative graph optimizer with edge rectification and a gated recurrent unit for enhanced rotation averaging.

Findings

Outperforms previous methods on real-world datasets

Effective in mitigating measurement noise and gauge freedom issues

Real-time performance with a lightweight model

Abstract

This paper proposes a deep recurrent Rotation Averaging Graph Optimizer (RAGO) for Multiple Rotation Averaging (MRA). Conventional optimization-based methods usually fail to produce accurate results due to corrupted and noisy relative measurements. Recent learning-based approaches regard MRA as a regression problem, while these methods are sensitive to initialization due to the gauge freedom problem. To handle these problems, we propose a learnable iterative graph optimizer minimizing a gauge-invariant cost function with an edge rectification strategy to mitigate the effect of inaccurate measurements. Our graph optimizer iteratively refines the global camera rotations by minimizing each node's single rotation objective function. Besides, our approach iteratively rectifies relative rotations to make them more consistent with the current camera orientations and observed relative rotations. Furthermore, we employ a gated recurrent unit to improve the result by tracing the temporal information of the cost graph. Our framework is a real-time learning-to-optimize rotation averaging graph optimizer with a tiny size deployed for real-world applications. RAGO outperforms previous traditional and deep methods on real-world and synthetic datasets. The code is available at https://github.com/sfu-gruvi-3dv/RAGO