Double Refinement Network for Room Layout Estimation

Abstract

Room layout estimation is a challenge of segmenting a cluttered room image into floor, walls and ceiling. We apply a Double Refinement Network (DRN) which has been successfully used to the monocular depth map estimation. Our method is the first not using encoder-decoder architecture for the room layout estimation. ResNet50 was utilized as a backbone for the network instead of VGG16 commonly used for the task, allowing the network to be more compact and faster. We introduced a special layout scoring function and layout ranking algorithm for key points and edges output. Our method achieved the lowest pixel and corner errors on the LSUN data set. The input image resolution is 224 * 224.