6D Pose Estimation of Transparent Object from Single RGB Image

• Main Authors: Munkhtulga Byambaa, Gou Koutaki, Lodoiravsal Choimaa
• Format: Article
• Language: English
• Published: FRUCT 2019-11-01
• Series: Proceedings of the XXth Conference of Open Innovations Association FRUCT
• Subjects: pose estimation; transparent object; synthetic data; deep neural network
• Online Access: https://fruct.org/publications/acm25/files/Bya.pdf

Transparent objects are one of the most common objects in everyday life. Estimating pose of these objects are required to pick and manipulate such objects. However, due to the absorption and refraction of light, it is hard to capture depth im- age of transparent object. In this paper, we address this problem using synthetic dataset to train deep neural network and estimate pose of known transparent objects. Synthetic dataset contains depth map of transparent object which we created in realistic looking environment. Also combining domain randomized and photorealistic images, we create desired amount of annotated data in order to network operate successfully against real world data. We conducted experiment on 3D printed transparent objects in the real environment. For future work, we are planning to build random bin picking system for transparent object.