Structured Camera Pose Estimation for Mosaic-Based Omnidirectional Imaging

This paper presents a novel structured camera pose estimation framework for mosaic-based omnidirectional imaging, i.e., producing a wide field of view (FoV) image that covers an entire sphere of the surroundings from a set of regular FoV images. With the effective utilization of geometric priors, the proposed framework exploits an individual image's connected structure while sequentially extracting correspondence between them. In the proposed framework, 2DSfM, a structure from motion method for multi-view images in structured 2D grids, is also proposed. Additionally, we propose a constraint term for rotation vectors in the bundle adjustment process that efficiently incorporates structural priors. We demonstrate our framework on structured omnidirectional image scenes and compare to existing frameworks. The experimental results show that our framework outperforms well-known conventional frameworks regarding both average reprojection error and reconstruction results.