3D layout estimation of general rooms based on ordinal semantic segmentation

Room layout estimation aims to predict the location and range of layout planes of interior spaces. Previous works treat each layout plane as an independent individual without considering the ordinal relation between walls, resulting the loss of the wall planes and the lack of integrity. This paper proposes a novel two-branch neural networks model to estimate 3D layouts of cuboid and non-cuboid room types. The model embeds the ordinal relation between layout planes into the layout segmentation branch through an proposed ordinal classification loss function, and outputs both pixel-level layout segmentation maps and layout plane parameter maps. Then, the instance-level plane parameters of each layout plane are determined by using an instance-aware pooling layer. Finally, the sharpness of layout edges of the 2D layout semantic segmentation map is optimized by using an improved depth map intersection algorithm. Furthermore, we annotate a large-scale 3D room layout estimation dataset, InteriorNet-Layout, to obtain a steady model. Experiments on synthesized real-world datasets show that the proposed method achieves faster calculation while maintaining high accuracy. Code is available at .