Peripheral Expansion of Depth Information via Layout Estimation with Fisheye Camera

Abstract

Consumer RGB-D cameras have become very useful in the last years, but their field of view is too narrow for certain applications. We propose a new hybrid camera system composed by a conventional RGB-D and a fisheye camera to extend the field of view over 180 $^{\circ }$ . With this system we have a region of the hemispherical image with depth certainty, and color data in the periphery that is used to extend the structural information of the scene. We have developed a new method to generate scaled layout hypotheses from relevant corners, combining the extraction of lines in the fisheye image and the depth information. Experiments with real images from different scenarios validate our layout recovery method and the advantages of this camera system, which is also able to overcome severe occlusions. As a result, we obtain a scaled 3D model expanding the original depth information with the wide scene reconstruction. Our proposal expands successfully the depth map more than eleven times in a single shot.