We propose DePR, a novel depth-guided single-view scene reconstruction framework that integrates instance-level diffusion priors. Our approach follows a compositional reconstruction paradigm, where individual objects are first generated before being arranged into a coherent scene. Unlike previous methods that solely use depth for object layout estimation during inference—thus underutilizing its rich geometric information—DePR leverages depth throughout both training and inference. Specifically, we introduce depth-guided conditioning to effectively encode shape priors into image-conditioned diffusion models. During inference, depth further aids in layout optimization and guided DDIM sampling, ensuring better alignment between reconstructed objects and the input image. Despite being trained on limited synthetic data, DePR achieves state-of-the-art performance and strong generalizability in single-view scene reconstruction, as demonstrated through evaluations on both synthetic and real-world datasets.