The estimation of the camera poses associated with a set of images commonly
relies on feature matches between the images. In contrast, we are the first to
address this challenge by using objectness regions to guide the pose estimation
problem rather than explicit semantic object detections. We propose Pose
Refiner Network (PoserNet) a light-weight Graph Neural Network to refine the
approximate pair-wise relative camera poses. PoserNet exploits associations
between the objectness regions - concisely expressed as bounding boxes - across
multiple views to globally refine sparsely connected view graphs. We evaluate
on the 7-Scenes dataset across varied sizes of graphs and show how this process
can be beneficial to optimisation-based Motion Averaging algorithms improving
the median error on the rotation by 62 degrees with respect to the initial
estimates obtained based on bounding boxes. Code and data are available at
this https URL

通过使用物体区域指导姿势估计问题，而不是显式语义物体检测，我们提出 Pose Refiner Network (PoserNet)—— 轻量级图神经网络来细化相机姿态的大致成对相对姿态。