Absolute rotation estimation is an important topic in 3D computer vision. Existing works in literature generally employ a multi-stage (at least two-stage) estimation strategy where multiple independent operations (feature matching, two-view rotation estimation, and rotation averaging) are implemented sequentially. However, such a multi-stage strategy inevitably leads to the accumulation of the errors caused by each involved operation, and degrades its final estimation on global rotations accordingly. To address this problem, we propose an End-to-end method for estimating Absolution Rotations from multi-view images based on deep neural Networks, called EAR-Net. The proposed EAR-Net consists of an epipolar confidence graph construction module and a confidence-aware rotation averaging module. The epipolar confidence graph construction module is explored to simultaneously predict pairwise relative rotations among the input images and their corresponding confidences, resulting in a weighted graph (called epipolar confidence graph). Based on this graph, the confidence-aware rotation averaging module, which is differentiable, is explored to predict the absolute rotations. Thanks to the introduced confidences of the relative rotations, the proposed EAR-Net could effectively handle outlier cases. Experimental results on three public datasets demonstrate that EAR-Net outperforms the state-of-the-art methods by a large margin in terms of accuracy and speed.

翻译：绝对旋转估计是三维计算机视觉中的一个重要课题。现有文献中的方法通常采用多阶段（至少两阶段）估计策略，其中多个独立操作（特征匹配、两视图旋转估计和旋转平均）被顺序执行。然而，这种多阶段策略不可避免地导致各操作产生的误差累积，并相应降低了对全局旋转的最终估计质量。为解决这一问题，我们提出了一种基于深度神经网络的端到端方法，用于从多视图图像中估计绝对旋转，称为EAR-Net。所提出的EAR-Net包含一个对极置信图构建模块和一个置信度感知的旋转平均模块。对极置信图构建模块旨在同时预测输入图像之间的成对相对旋转及其对应置信度，从而生成一个加权图（称为对极置信图）。基于该图，可微分的置信度感知旋转平均模块被用于预测绝对旋转。得益于引入的相对旋转置信度，所提出的EAR-Net能够有效处理异常情况。在三个公开数据集上的实验结果表明，EAR-Net在精度和速度上均大幅优于现有最先进方法。