  We present DiffInDScene, a novel framework for tackling the problem of
high-quality 3D indoor scene generation, which is challenging due to the
complexity and diversity of the indoor scene geometry. Although diffusion-based
generative models have previously demonstrated impressive performance in image
generation and object-level 3D generation, they have not yet been applied to
room-level 3D generation due to their computationally intensive costs. In
DiffInDScene, we propose a cascaded 3D diffusion pipeline that is efficient and
possesses strong generative performance for Truncated Signed Distance Function
(TSDF). The whole pipeline is designed to run on a sparse occupancy space in a
coarse-to-fine fashion. Inspired by KinectFusion's incremental alignment and
fusion of local TSDF volumes, we propose a diffusion-based SDF fusion approach
that iteratively diffuses and fuses local TSDF volumes, facilitating the
generation of an entire room environment. The generated results demonstrate
that our work is capable to achieve high-quality room generation directly in
three-dimensional space, starting from scratch. In addition to the scene
generation, the final part of DiffInDScene can be used as a post-processing
module to refine the 3D reconstruction results from multi-view stereo.
According to the user study, the mesh quality generated by our DiffInDScene can
even outperform the ground truth mesh provided by ScanNet. Please visit our
project page for the latest progress and demonstrations:
https://github.com/AkiraHero/diffindscene.
