Learning to Predict High-Quality Edge Maps for Room Layout Estimation

The goal of room layout estimation is to predict the three-dimensional box that represents the room spatial structure from a monocular image. In this paper, a deconvolution network is trained first to predict the edge map of a room image. Compared to the previous fully convolutional networks, the proposed deconvolution network has a multilayer deconvolution process that can refine the edge map estimate layer by layer. The deconvolution network also has fully connected layers to aggregate the information of every region throughout the entire image. During the layout generation process, an adaptive sampling strategy is introduced based on the obtained high-quality edge maps. Experimental results prove that the learned edge maps are highly reliable and can produce accurate layouts of room images.