论文标题
物体6D姿势估计，非本地关注
Object 6D Pose Estimation with Non-local Attention
论文作者
论文摘要
在本文中，我们解决了从单个RGB图像中估算6D对象姿势的具有挑战性的任务。由基于深度学习的对象检测方法的激励，我们提出了一个简洁有效的网络，将6D对象构成参数估计到对象检测框架中。此外，为了对闭塞进行更强大的估计，还引入了非本地自我注意力的模块。实验结果表明，所提出的方法达到YCB-VIDEO和LineMod数据集的最新性能。
In this paper, we address the challenging task of estimating 6D object pose from a single RGB image. Motivated by the deep learning based object detection methods, we propose a concise and efficient network that integrate 6D object pose parameter estimation into the object detection framework. Furthermore, for more robust estimation to occlusion, a non-local self-attention module is introduced. The experimental results show that the proposed method reaches the state-of-the-art performance on the YCB-video and the Linemod datasets.
