Abstract
Estimation of room layout suffers from heavy occlusions and clutters in indoor scenes. In this paper, we propose a deep network that combines textures and geometric hints to predict the surface layout from a single image. Our method consists of three steps. First, depths and normals are extracted from the input RGB image. Secondly, a multi-channel FCN (MC-FCN) is presented to integrate these geometric hints for semantic surface segmentation. Thirdly, an optimization framework is adopted to refine the layout estimation. The results on two commonly used benchmark datasets demonstrate the robustness of our method on complex scenes.
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