As multi-view reconstruction techniques evolve, they accomplish to reconstruct larger environments. This is possible due to the availability of vast image collections of the target scenes. Within the next years it will be necessary to account for all available sources of visual information to supply future 3D reconstruction approaches. Accordingly, Structure from Motion (SfM) algorithms will need to handle such variety of image sources, i.e. perspective, wide-angle or spherical images. Although SfM for perspective and spherical images as well as catadioptric systems have already been studied, state of the art algorithms are not able to deal with these images simultaneously. To close this gap, we developed SPHERA, a unifying SfM framework designed for central projection cameras. It uses a sphere as underlying model, allowing single effective viewpoint vision systems to be treated in a unified way. We validate our framework with quantitative evaluations on synthetic spherical as well as real perspective, spherical and hybrid image datasets. Results show that SPHERA is a powerful framework to support upcoming algorithms and applications on large scale 3D reconstruction.
Recently, Gava and Stricker REF proposed a unified SFM framework that allows uniform treatment of central projection cameras using a sphere as the underlying model.
SPHERA - A Unifying Structure from Motion Framework for Central Projection Cameras
VISAPP
Computer Science