Structured Camera Pose Estimation for Mosaic-Based Omnidirectional Imaging

Abstract

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.

Camera pose estimation framework for array‐structured images

“…We extend our baseline framework [25] to a general mosaic-based array-structured image set from omnidirectional imaging settings. The proposed framework can work without the loop closure process from previous works.…”