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Limited by the computational efficiency and accuracy, generating complex 3Dscenes remains a challenging problem for existing generation networks. In thiswork, we propose DepthGAN, a novel method of generating depth maps with onlysemantic layouts as input. First, we introduce a well-designed cascade oftransformer blocks as our generator to capture the structural correlations indepth maps, which makes a balance between global feature aggregation and localattention. Meanwhile, we propose a cross-attention fusion module to guide edgepreservation efficiently in depth generation, which exploits additionalappearance supervision information. Finally, we conduct extensive experimentson the perspective views of the Structured3d panorama dataset and demonstratethat our DepthGAN achieves superior performance both on quantitative resultsand visual effects in the depth generation task.Furthermore, 3D indoor scenescan be reconstructed by our generated depth maps with reasonable structure andspatial coherency.

DepthGAN: GAN-based Depth Generation of Indoor Scenes from Semantic Layouts