BuilDiff: 3D Building Shape Generation using Single-Image Conditional
  Point Cloud Diffusion Models
Yao Wei George Vosselman Michael Ying Yang
3D building generation with low data acquisition costs, such as single
image-to-3D, becomes increasingly important. However, most of the existing
single image-to-3D building creation works are restricted to those images with
specific viewing angles, hence they are difficult to scale to general-view
images that commonly appear in practical cases. To fill this gap, we propose a
novel 3D building shape generation method exploiting point cloud diffusion
models with image conditioning schemes, which demonstrates flexibility to the
input images. By cooperating two conditional diffusion models and introducing a
regularization strategy during denoising process, our method is able to
synthesize building roofs while maintaining the overall structures. We validate
our framework on two newly built datasets and extensive experiments show that
our method outperforms previous works in terms of building generation quality.