论文标题
野外类别级别6D对象姿势估计：一种半监督的学习方法和一种新数据集
Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset
论文作者
论文摘要
6D对象姿势估计是计算机视觉和机器人研究中的基本问题之一。尽管最近在同一类别内将姿势估计概括为新的对象实例（即类别级别的6D姿势估计）方面已做出了许多努力，但考虑到有限的带注释数据，它仍然在受约束的环境中受到限制。在本文中，我们收集了Wild6D，这是一种具有不同实例和背景的新的未标记的RGBD对象视频数据集。我们利用这些数据将类别级别的6D对象在野外进行半监督学习。我们提出了一个新模型，称为呈现姿势估计网络reponet，该模型使用带有合成数据的自由地面真实性共同训练，以及对现实世界数据的Silhouette匹配目标函数。在不使用实际数据上的任何3D注释的情况下，我们的方法的表现优于上一个数据集上的最先进方法，而我们的WILD6D测试集（带有手动注释进行评估）则优于较大的边距。带有WILD6D数据的项目页面：https：//oasisyang.github.io/semi-pose。
6D object pose estimation is one of the fundamental problems in computer vision and robotics research. While a lot of recent efforts have been made on generalizing pose estimation to novel object instances within the same category, namely category-level 6D pose estimation, it is still restricted in constrained environments given the limited number of annotated data. In this paper, we collect Wild6D, a new unlabeled RGBD object video dataset with diverse instances and backgrounds. We utilize this data to generalize category-level 6D object pose estimation in the wild with semi-supervised learning. We propose a new model, called Rendering for Pose estimation network RePoNet, that is jointly trained using the free ground-truths with the synthetic data, and a silhouette matching objective function on the real-world data. Without using any 3D annotations on real data, our method outperforms state-of-the-art methods on the previous dataset and our Wild6D test set (with manual annotations for evaluation) by a large margin. Project page with Wild6D data: https://oasisyang.github.io/semi-pose .
