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Learning-based 3D Object Localization and Pose Estimation: A Survey
PhD Qualifying Examination Title: "Learning-based 3D Object Localization and Pose Estimation: A Survey" by Mr. Yisheng HE Abstract: 3D object localization and pose estimation targets to determine the 3D spatial location and orientation of object instances in captured sensor data, which plays a significant role in numerous real-world applications, such as robotic manipulation, autonomous driving and augmented reality. According to different problem formulation, it can be further divided into two tasks: 3D object localization, also named 3D object detection and object 6D pose estimation. Given sensor data of a scene, 3D object detection returns the spatial location and extent of each object instance via a 3D bounding box and object 6D pose estimation outputs the precise 3D location and orientation of known objects. With the explosive growth of deep learning techniques in recent years, many learning-based approaches are introduced into this field and some major improvements have been achieved. In this survey, we first give a brief review of the deep learning techniques for automatically learning of object feature representations. Then, we introduce the recent achievements of learning-based 3D object localization and 6D pose estimation respectively. Finally, we conclude some limitations and open problems in current approaches and discuss possible future research directions. Date: Friday, 14 February 2020 Time: 3:30pm - 5:30pm Zoom Meeting: https://hkust.zoom.com.cn/j/561103463 Committee Members: Prof. Long Quan (Supervisor) Dr. Xiaojuan Ma (Chairperson) Dr. Qifeng Chen Prof. Chiew-Lan Tai **** ALL are Welcome ****