  While the existing deep learning-based room layout estimation techniques
demonstrate good overall accuracy, they are less effective for distant
floor-wall boundary. To tackle this problem, we propose a novel
uncertainty-guided approach for layout boundary estimation introducing new
two-stage CNN architecture termed U2RLE. The initial stage predicts both
floor-wall boundary and its uncertainty and is followed by the refinement of
boundaries with high positional uncertainty using a different, distance-aware
loss. Finally, outputs from the two stages are merged to produce the room
layout. Experiments using ZInD and Structure3D datasets show that U2RLE
improves over current state-of-the-art, being able to handle both near and far
walls better. In particular, U2RLE outperforms current state-of-the-art
techniques for the most distant walls.
