论文标题
对6D对象姿势估计的弱监督学习键盘的学习
Weakly Supervised Learning of Keypoints for 6D Object Pose Estimation
论文作者
论文摘要
6D对象姿势估计的最先进方法需要大量的标记数据来训练深层网络。但是，收购6D对象构成注释是乏味的，大量劳动力密集。为了减轻这个问题，我们提出了基于2D键盘检测的弱监督的6D对象姿势估计方法。我们的方法仅在图像对上训练其观点之间的相对变换。具体来说，我们分配了一组任意选择的3D关键点来表示每个未知目标3D对象，并学习一个网络以检测符合相对摄像机视点的2D投影。在推断期间，我们的网络首先从查询图像和给定标记的参考图像中注入2D关键。然后，我们使用这些2D关键点，并使用从训练中保留的任意选择的3D键点来推断6D对象姿势。广泛的实验表明，我们的方法通过最先进的完全监督的方法实现了可比的性能。
State-of-the-art approaches for 6D object pose estimation require large amounts of labeled data to train the deep networks. However, the acquisition of 6D object pose annotations is tedious and labor-intensive in large quantity. To alleviate this problem, we propose a weakly supervised 6D object pose estimation approach based on 2D keypoint detection. Our method trains only on image pairs with known relative transformations between their viewpoints. Specifically, we assign a set of arbitrarily chosen 3D keypoints to represent each unknown target 3D object and learn a network to detect their 2D projections that comply with the relative camera viewpoints. During inference, our network first infers the 2D keypoints from the query image and a given labeled reference image. We then use these 2D keypoints and the arbitrarily chosen 3D keypoints retained from training to infer the 6D object pose. Extensive experiments demonstrate that our approach achieves comparable performance with state-of-the-art fully supervised approaches.
