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Directly generating scenes from satellite imagery offers excitingpossibilities for integration into applications like games and map services.However, challenges arise from significant view changes and scene scale.Previous efforts mainly focused on image or video generation, lackingexploration into the adaptability of scene generation for arbitrary views.Existing 3D generation works either operate at the object level or aredifficult to utilize the geometry obtained from satellite imagery. To overcomethese limitations, we propose a novel architecture for direct 3D scenegeneration by introducing diffusion models into 3D sparse representations andcombining them with neural rendering techniques. Specifically, our approachgenerates texture colors at the point level for a given geometry using a 3Ddiffusion model first, which is then transformed into a scene representation ina feed-forward manner. The representation can be utilized to render arbitraryviews which would excel in both single-frame quality and inter-frameconsistency. Experiments in two city-scale datasets show that our modeldemonstrates proficiency in generating photo-realistic street-view imagesequences and cross-view urban scenes from satellite imagery.

Sat2Scene: 3D Urban Scene Generation from Satellite Images with Diffusion