Room reconstruction from a single spherical image by higher-order energy minimization

We propose a method for understanding a room from a single spherical image, i.e., reconstructing and identifying structural planes forming the ceiling, the floor, and the walls in a room. A spherical image records the light that falls onto a single viewpoint from all directions and does not require correlating geometrical information from multiple images, which facilitates robust and precise reconstruction of the room structure. In our method, we detect line segments from a given image, and classify them into two groups: segments that form the boundaries of the structural planes and those that do not. We formulate this problem as a higher-order energy minimization problem that combines the various measures of likelihood that one, two, or three line segments are part of the boundary. We minimize the energy with graph cuts to identify segments forming boundaries, from which we estimate structural the planes in 3D. Experimental results on synthetic and real images confirm the effectiveness of the proposed method.