Building Sparse 3d Representations from a Set of Calibrated Panoramic Images

Virtual navigation in remote environments can be achieved by building an image-based model made of multiple panoramas gathered from cameras moving around the scene. In such models, it could be useful to acquire knowledge of the 3D structure of the scene. In this paper, we propose a method that constructs a sparse but rich 3D representation of a scene, given a set of calibrated panoramic images. The proposed method is a heuristic search algorithm that, given calibrated panoramic images, finds 3D points that correspond to the surfaces of objects in the scene. The algorithm constructs a set of 3D points by searching for matching edge pixels in pairs of images using the epipolar constraint. Empirical results show that the proposed method performs well at locating 3D points of interest in different scenes.