6D object pose (3D rotation and translation) estimation from RGB-D image is an important and challenging task in computer vision and has been widely applied in a variety of applications such as robotic manipulation, autonomous driving, augmented reality etc. Prior works extract global feature or reason about local appearance from an individual frame, which neglect the spatial geometric relevance between two frames, limiting their performance for occluded or truncated objects in heavily cluttered scenes. In this paper, we present a dual-stream network for estimating 6D pose of a set of known objects from RGB-D images. Our novelty stands in contrast to prior work that learns latent geometric consistency in pairwise dense feature representations from multiple observations of the same objects in a self-supervised manner. We show in experiments that our method outperforms state-of-the-art approaches on 6D object pose estimation in two challenging datasets, YCB-Video and LineMOD.

从RGB-D图像估计6D对象姿态（3D旋转和平移）是计算机视觉中一项重要且具有挑战性的任务，已广泛应用于各种应用中，例如机器人操纵，自动驾驶，增强现实等。来自单个帧的局部外观的特征或原因，它忽略了两个帧之间的空间几何相关性，从而限制了它们在杂乱无章的场景中对被遮挡或被截断的对象的性能。在本文中，我们提出了一种双流网络，用于从RGB-D图像估计一组已知对象的6D姿态。我们的新颖性与先前的工作形成了鲜明对比，之前的工作以自监督的方式从对同一对象的多次观察中学习成对的密集特征表示中的潜在几何一致性。