论文标题
DGECN：用于端到端6D姿势估计的深度引导边缘卷积网络
DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D Pose Estimation
论文作者
论文摘要
单眼6D姿势估计是计算机视觉中的基本任务。现有作品通常通过建立对应关系并利用RANSAC算法来计算6个自由度（6DOF）姿势来采用两阶段的管道。最近的工作试图集成可区分的RANSAC算法，以实现端到端的6D姿势估计。但是，其中大多数几乎不考虑3D空间中的几何特征，并且在执行可区分的RANSAC算法时忽略了拓扑提示。为此，我们为6D姿势估计任务提出了一个深度引导的边缘卷积网络（DGECN）。我们从以下三个方面做出了努力：1）我们采用估计深度信息的优点，以指导对应关系 - 取消过程和使用几何信息的级联可区分的RANSAC算法。 2）我们利用估计深度图的不确定性来提高输出6D姿势的准确性和鲁棒性。 3）我们通过边缘卷积提出了一种可区分的透视-N点（PNP）算法，以探索2d-3d对应关系之间的拓扑关系。实验表明，我们提出的网络在有效性和效率方面都优于当前作品。
Monocular 6D pose estimation is a fundamental task in computer vision. Existing works often adopt a two-stage pipeline by establishing correspondences and utilizing a RANSAC algorithm to calculate 6 degrees-of-freedom (6DoF) pose. Recent works try to integrate differentiable RANSAC algorithms to achieve an end-to-end 6D pose estimation. However, most of them hardly consider the geometric features in 3D space, and ignore the topology cues when performing differentiable RANSAC algorithms. To this end, we proposed a Depth-Guided Edge Convolutional Network (DGECN) for 6D pose estimation task. We have made efforts from the following three aspects: 1) We take advantages ofestimated depth information to guide both the correspondences-extraction process and the cascaded differentiable RANSAC algorithm with geometric information. 2)We leverage the uncertainty ofthe estimated depth map to improve accuracy and robustness ofthe output 6D pose. 3) We propose a differentiable Perspective-n-Point(PnP) algorithm via edge convolution to explore the topology relations between 2D-3D correspondences. Experiments demonstrate that our proposed network outperforms current works on both effectiveness and efficiency.
