The goal of this work is to recover the spatial layout of indoor environments from omnidirectional images assuming a Manhattan world structure. We propose a new method for scene structure recovery from a single image. This method is based on the line extraction for omnidirectional images, line classification, and vanishing points estimation combined with a new hierarchical expansion procedure for detecting floor and wall boundaries. Each single omnidirectional image independently provides a useful hypothesis of the 3D scene structure. In order to enhance the robustness and accuracy of this single image-based hypothesis, we extend this estimation with a new homography-based procedure applied to the various hypotheses obtained along the sequence of consecutive images. A key point in this contribution is the use of geometrical constraints for computing the homographies from a single line of the floor. The homography parametrization proposed allows the design of a matching-free method for spatial layout propagation along a sequence of images. Experimental results show single image layout recovery performance and the improvement obtained with the propagation of the hypothesis through the image sequence.
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