Synthesizing 3D Scenes via Diffusion Model that Incorporates Indoor Scene Characteristics

Diffusion model has been used in indoor scene synthesis and has made significant progress. Current works encode an indoor scene as a top-down view of the room, a list of objects, and their world co-ordinates and orientation. In this paper, we develop a diffusion-based training and synthetic method which incorporates indoor scene ''characteristics''. Firstly, we calculate the relative transformations among objects to capture the local characteristics of the scene. We send this relative transformation into the self-attention layer of the denoising network as ''relative positional encoding''. Secondly, we use room guidance to guide the objects to fit the room's geometry. This improvement uses the room's characteristics to solve the physical collision problem occurring in former diffusion-based works, while preserving plausibilities. Experiments show that our improvements improve the scene variety and quality.