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3D building generation with low data acquisition costs, such as singleimage-to-3D, becomes increasingly important. However, most of the existingsingle image-to-3D building creation works are restricted to those images withspecific viewing angles, hence they are difficult to scale to general-viewimages that commonly appear in practical cases. To fill this gap, we propose anovel 3D building shape generation method exploiting point cloud diffusionmodels with image conditioning schemes, which demonstrates flexibility to theinput images. By cooperating two conditional diffusion models and introducing aregularization strategy during denoising process, our method is able tosynthesize building roofs while maintaining the overall structures. We validateour framework on two newly built datasets and extensive experiments show thatour method outperforms previous works in terms of building generation quality.

BuilDiff: 3D Building Shape Generation using Single-Image Conditional Point Cloud Diffusion Models