Discriminative learning with latent variables for cluttered indoor scene understanding

Holistic scene understanding has attracted much attention in computer vision. One of the goals is to make use of image context to help improve traditional vision tasks, such as object detection. Due to the effect of perspective projection, a set of parallel lines in 3D are either parallel or intersect at a common point in the 2D image. The points where sets of 3D parallel lines meet in 2D are called vanishing points. Most indoor scenes are characterized by three vanishing points. Complexity of the algorithm depends on a number of design choices. For example, a larger number of segments (dimensionality of h) may be able to model the clutter at a finer scale but could potentially make inference slow to converge as introducing more latent variables generally increases the number of iterations required by the ICM algorithm.