Low-Quality Rendering-Driven 6D Object Pose Estimation from Single RGB Image

Abstract

The 6D object pose obtained from single RGB image has broad applications such as robotic manipulation and virtual reality. Among many existing methods, the deep learning-based approaches for object pose estimation from single RGB image are widely used. However, they often require a large amount of training data, which has great challenges in high cost of data collection and lack of 3D information. In this paper, we introduce an object pose estimation architecture that takes a single RGB image as input and directly outputs rotation angles and translation vectors. A data generation pipeline that applies the idea of domain randomization is used to generate millions of low-quality rendering images. Then the pose estimation is realized by fusing the architecture and the domain randomization approach to utilize the generated information and low the data collection cost. We synthesized a big dataset called Pose6DDR whose images are similar to those in the LineMod dataset. Experiments demonstrated the effectiveness of the proposed 6D object pose estimation architecture as compared to the relevant competing technologies.