Indoor Structure Understanding from Single 360° Cylindrical Panoramic Image

We address the problem of room structure recognition from a 360 degrees cylindrical panorama. The proposed approach first transforms the original panorama into four perspective projected sub-images, then predicts the final layout based on a holistic parameterization framework via candidate evaluation using a linear scoring function, whose weights are trained on a normal indoor image data set. Experiments show that the proposed algorithm is capable of producing reasonable predictions which can be used for further applications such as 3D reconstruction.