An example of camera 6D pose estimation is illustrated in figure. Given an image of an object and a robotic arm with a camera mounted at its end, the task is to estimate the camera pose from which the image was captured, starting from an initial pose.

When a 3D model of the object is available, such as a NeRF model, the problem has been explored in iNeRF [1]. However, if the object is unknown, the question arises: Is there an effective online search strategy? While a straightforward approach might involve using random search and using gradient-based optimization in the vicinity of the correct answer, such methods may not be efficient.

A promising direction is to introduce shape priors, which provide an intuitive way to guide the search process and improve efficiency.

Enrolled in computer science or similar MSc program in and around Bonn/Cologne

Familiarity with 3D perception and viewpoint planning

Experience with shape completion and deep learning

Programming experience with C++, Python, and ROS (Robot Operating System)

Enthusiasm for real-world robot deployment and scientific publishing of results

[1] Yen-Chen, Lin, et al. "iNeRF: Inverting Neural Radiance Fields for Pose Estimation." 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2021