Towards detection of orthogonal planes in monocular images of indoor environments

In this paper, we describe the components of a novel algorithm for the extraction of dominant orthogonal planar structures from monocular images taken in indoor environments. The basic building block of our approach is the use of vanishing points and vanishing lines imposed by the frequently observed dominance of three mutually orthogonal vanishing directions in man-made world. Vanishing points are found by an improved approach, taking no assumptions on known internal or external camera parameters. The problem of detecting planar patches is attacked using a probabilistic framework, searching for the maximum a posteriori probability (MAP) in a Markov Random Field (MRF). For this, we propose a novel formulation fusing geometric information obtained from vanishing points and features, such as rectangles and partial rectangles, together with a color-homogeneity criteria imposed by an image over-segmentation. The method was evaluated on a set of images exhibiting largely varying characteristics concerning image quality and scene complexity. Experiments show that the method, despite the variations, works in a stable manner and that its performance compares favorably to the state-of-the-art.