Pose Estimation Based on 3D Models
This work addresses pose estimation for computer vision applications, but it appears incremental as it builds on existing methods with iterative improvements.
The paper tackled the problem of object pose estimation in real images by using a rendered image training set and a patch-based multi-class classification algorithm with an iterative approach, achieving state-of-the-art performance.
In this paper, we proposed a pose estimation system based on rendered image training set, which predicts the pose of objects in real image, with knowledge of object category and tight bounding box. We developed a patch-based multi-class classification algorithm, and an iterative approach to improve the accuracy. We achieved state-of-the-art performance on pose estimation task.