Realistic conditional 3D scene synthesis significantly enhances and
accelerates the creation of virtual environments, which can also provide
extensive training data for computer vision and robotics research among other
applications. Diffusion models have shown great performance in related
applications, e.g., making precise arrangements of unordered sets. However,
these models have not been fully explored in floor-conditioned scene synthesis
problems. We present MiDiffusion, a novel mixed discrete-continuous diffusion
model architecture, designed to synthesize plausible 3D indoor scenes from
given room types, floor plans, and potentially pre-existing objects. We
represent a scene layout by a 2D floor plan and a set of objects, each defined
by its category, location, size, and orientation. Our approach uniquely
implements structured corruption across the mixed discrete semantic and
continuous geometric domains, resulting in a better conditioned problem for the
reverse denoising step. We evaluate our approach on the 3D-FRONT dataset. Our
experimental results demonstrate that MiDiffusion substantially outperforms
state-of-the-art autoregressive and diffusion models in floor-conditioned 3D
scene synthesis. In addition, our models can handle partial object constraints
via a corruption-and-masking strategy without task specific training. We show
MiDiffusion maintains clear advantages over existing approaches in scene
completion and furniture arrangement experiments.

MiDiffusion 是一种新的混合离散 - 连续扩散模型架构，用于根据房间类型、平面图和可能的预存在对象合成逼真的室内 3D 场景，相比于自回归模型和扩散模型，在楼层条件下的 3D 场景合成表现出明显的优势，并可通过破坏和遮蔽策略处理部分对象约束，无需专门训练。