3D Room Layout Estimation From a Single RGB Image

3D layout is crucial for scene understanding and reconstruction, and very useful in applications like real estate and furniture design. In this paper, we propose a fully automatic solution to estimate 3D layout of an indoor scene from a single 2D image. Our technique contains two key components. Firstly, we train a neural network that directly estimates room structure lines from the input image. Secondly, we propose a novel technique to automatically identify the layout topology of an input image, followed by a nonlinear optimization with equality constraints to estimate the final 3D layout of a scene. Based on our knowledge, this is the first fully automatic technique to achieve single image-based 3D layout estimation of an indoor scene. We evaluate our method on the public datasets <inline-formula><tex-math notation="LaTeX">$LSUN$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$Hedau$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$3DGP$</tex-math></inline-formula> and the results show that the proposed method achieves accurate 3D layout reconstruction on various images with different layout topologies.