  The task of estimating the spatial layout of cluttered indoor scenes from a
single RGB image is addressed in this work. Existing solutions to this problems
largely rely on hand-craft features and vanishing lines, and they often fail in
highly cluttered indoor rooms. The proposed coarse-to-fine indoor layout
estimation (CFILE) method consists of two stages: 1) coarse layout estimation;
and 2) fine layout localization. In the first stage, we adopt a fully
convolutional neural network (FCN) to obtain a coarse-scale room layout
estimate that is close to the ground truth globally. The proposed FCN considers
combines the layout contour property and the surface property so as to provide
a robust estimate in the presence of cluttered objects. In the second stage, we
formulate an optimization framework that enforces several constraints such as
layout contour straightness, surface smoothness and geometric constraints for
layout detail refinement. Our proposed system offers the state-of-the-art
performance on two commonly used benchmark datasets.
