Pose Estimation of Uncooperative Unknown Space Objects from a Single Image

Abstract

Estimating the 3D pose of the space object from a single image is an important but challenging work. Most of the existing methods estimate the 3D pose of known space objects and assume that the detailed geometry of a specific object is known. These methods are not available for unknown objects without the known geometry of the object. In contrast to previous works, this paper devotes to estimate the 3D pose of the unknown space object from a single image. Our method estimates not only the pose but also the shape of the unknown object from a single image. In this paper, a hierarchical shape model is proposed to represent the prior structure information of typical space objects. On this basis, the parameters of the pose and shape are estimated simultaneously for unknown space objects. Experimental results demonstrate the effectiveness of our method to estimate the 3D pose and infer the geometry of unknown typical space objects from a single image. Moreover, experimental results show the advantage of our approach over the methods relying on the known geometry of the object.

Deep learning based six‐dimensional pose estimation in virtual reality

“…If the camera's internal parameters are not accurate, the measurement error will be out of control. Monocular vision measurement system 27 includes the measurement of the cooperative and noncooperative targets. 28 In short, cooperative targets are ones with communication during measurement, while noncooperative targets are ones not directly contacted in the measurement process.…”