Thinking inside the box: Using appearance models and context based on room geometry

In this paper we show that a geometric representation of an object occurring in indoor scenes, along with rich scene structure can be used to produce a detector for that object in a single image. Using perspective cues from the global scene geometry, we first develop a 3D based object detector. This detector is competitive with an image based detector built using state-of-the-art methods; however, combining the two produces a notably improved detector, because it unifies contextual and geometric information. We then use a probabilistic model that explicitly uses constraints imposed by spatial layout - the locations of walls and floor in the image - to refine the 3D object estimates. We use an existing approach to compute spatial layout [1], and use constraints such as objects are supported by floor and can not stick through the walls. The resulting detector (a) has significantly improved accuracy when compared to the state-of-the-art 2D detectors and (b) gives a 3D interpretation of the location of the object, derived from a 2D image. We evaluate the detector on beds, for which we give extensive quantitative results derived from images of real scenes.