PhysRecon: Simulation-in-the-Loop Scene Reconstruction for Physically Credible Embodied Data Generation from Single Images

Single-view 3D reconstruction and 6D pose estimation often produces visually plausible yet physically infeasible scenes, such as floating objects, unstable stacking and unreasonable object scales, while suffering from incomplete geometry caused by visual occlusion. To address these issues, we propose PhysRecon, a physics-guided closed-loop framework for physically credible single-image 3D scene reconstruction. Different from conventional purely visual reconstruction methods, our pipeline integrates occlusion-aware visual completion and physics-based iterative refinement. Specifically, we adopt an occlusion-aware inpainting module to recover complete object geometry under occluded observations. Instead of directly predicting object mass, our framework estimates physical density and calculates mass via reconstructed mesh volume, enabling physically consistent parameter modeling. We further design a failure-driven local sampling strategy combined with quasi-static physical simulation and physical violation auditing, which iteratively corrects unreasonable object scales and poses while freezing well-reconstructed objects. Evaluated on HomebrewedDB and LM-O benchmarks, our method outperforms state-of-the-art visual reconstruction baselines in both geometric reconstruction accuracy and 6D pose estimation. Extensive quantitative ablation and qualitative visualization further validate the effectiveness of each core component, demonstrating that PhysRecon can generate visually faithful and physically reasonable 3D scenes from a single RGB image.