Room Layout Estimation based on Planar Semantic Segmentation

Layout estimation is one of fundamental tasks for understanding indoor scenes. In a single image of indoor scenes, key information, such as key points and boundaries for inferring the layout is often severely occluded or completely invisible. In this paper, a novel end-to-end deep learning network, which is extended from Xception module, is constructed for segmentation of semantic planes to infer the room layout structure. Moreover, the network contains an optimized depth wise separable convolution (DSC) in Xception module and a reinforced spatial attention module (SAM), so that it obtained good learning ability in severely occluded areas. Tests on public datasets show that the proposed network has a lower pixel error (PE) and higher generalization compared with existing algorithms. Ablation experiments prove that optimized DSC and SAM in the network are effective in improving the accuracy of layout estimation.