论文标题
Rago：用于多旋转平均的复发图优化器
RAGO: Recurrent Graph Optimizer For Multiple Rotation Averaging
论文作者
论文摘要
本文提出了用于多个旋转平均（MRA）的深度复发平均图形优化器（RAGO）。传统的基于优化的方法通常由于损坏和嘈杂的相对测量而无法产生准确的结果。最近的基于学习的方法将MRA视为回归问题，而这些方法由于仪表自由问题而对初始化敏感。为了解决这些问题，我们提出了一个可学习的迭代图优化器，通过边缘整流策略最小化规格不变的成本函数，以减轻不准确测量的效果。我们的图形优化器迭代通过最大程度地降低每个节点的单旋目标函数来完善全局摄像机旋转。此外，我们的方法迭代地纠正了相对旋转，以使它们与当前的摄像头方向和观察到的相对旋转更加一致。此外，我们采用封闭式的复发单元来通过追踪成本图的时间信息来改善结果。我们的框架是一种实时学习，以优化的旋转平均图形优化器，其尺寸很小，用于现实世界中的应用程序。 Rago在现实世界和合成数据集上胜过以前的传统和深度方法。该代码可从https://github.com/sfu-gruvi-3dv/rago获得
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
