  Limited by the computational efficiency and accuracy, generating complex 3D
scenes remains a challenging problem for existing generation networks. In this
work, we propose DepthGAN, a novel method of generating depth maps with only
semantic layouts as input. First, we introduce a well-designed cascade of
transformer blocks as our generator to capture the structural correlations in
depth maps, which makes a balance between global feature aggregation and local
attention. Meanwhile, we propose a cross-attention fusion module to guide edge
preservation efficiently in depth generation, which exploits additional
appearance supervision information. Finally, we conduct extensive experiments
on the perspective views of the Structured3d panorama dataset and demonstrate
that our DepthGAN achieves superior performance both on quantitative results
and visual effects in the depth generation task.Furthermore, 3D indoor scenes
can be reconstructed by our generated depth maps with reasonable structure and
spatial coherency.
