  We present DiffuScene for indoor 3D scene synthesis based on a novel scene
configuration denoising diffusion model. It generates 3D instance properties
stored in an unordered object set and retrieves the most similar geometry for
each object configuration, which is characterized as a concatenation of
different attributes, including location, size, orientation, semantics, and
geometry features. We introduce a diffusion network to synthesize a collection
of 3D indoor objects by denoising a set of unordered object attributes.
Unordered parametrization simplifies and eases the joint distribution
approximation. The shape feature diffusion facilitates natural object
placements, including symmetries. Our method enables many downstream
applications, including scene completion, scene arrangement, and
text-conditioned scene synthesis. Experiments on the 3D-FRONT dataset show that
our method can synthesize more physically plausible and diverse indoor scenes
than state-of-the-art methods. Extensive ablation studies verify the
effectiveness of our design choice in scene diffusion models.
