부산대학교 도서관

6DoF Object Pose Estimation plays important roles in many computer vision tasks such as Augmented Reality and robotics. During the past decade, the community has been witnessing a great improvement of the pose estimation algorithms. In the age of deep learning, the dominating feature point matching methods have been gradually replaced by various one-shot methods or point-predicting methods. However, the good performances are usually obtained on standard benchmark datasets, with even lighting and few occlusions. In this work, we build a new ‘in-the-wild’ image dataset for the task of 6-DoF object pose estimation, termed Wild-6DoF. This data contains fully-annotated images of 5 real-life objects captured in various complex scenes. We compare some state-of-the-art algorithms as well as a deep-learning based point matching method on some commonly-adopted dataset and the newly-proposed dataset. We found that the state-of-the-art methods, which performs well on the well-controlled datasets, could not beat the "old-fashioned" point-matching method. This observation could reminds the computer vision community that the potential of the conventional approach, especially in the real-life applications.