DepR: Depth Guided Single-view Scene Reconstruction with Instance-level Diffusion

We propose DepR, a depth-guided single-view scene reconstruction framework that integrates instance-level diffusion within a compositional paradigm. Instead of reconstructing the entire scene holistically, DepR generates individual objects and subsequently composes them into a coherent 3D layout.

Unlike previous methods that use depth solely for object layout estimation during inference and therefore fail to fully exploit its rich geometric information, DepR leverages depth throughout both training and inference. Specifically, we introduce depth-guided conditioning to effectively encode shape priors into diffusion models. During inference, depth further guides DDIM sampling and layout optimization, enhancing alignment between the reconstruction and the input image. Despite being trained on limited synthetic data, DepR achieves state-of-the-art performance and demonstrates strong generalization in singleview scene reconstruction, as shown through evaluations on both synthetic and real-world datasets.

Overview of our DepR. Depth is utilized in three key stages: 1) to back-project features to condition the latent tri-plane diffusion model to generate complete 3D shapes; 2) to guide the diffusion sampling process via gradients from a depth loss; and 3) to optimize object poses via layout loss for accurate scene composition.