PHYRECON is a novel approach that integrates differentiable rendering and physics simulation to learn implicit surface representations for 3D scene reconstruction. The method addresses the limitations of existing methods by incorporating physics constraints, which are often overlooked in neural implicit representations. Key contributions include: 1. **Differentiable Particle-Based Physical Simulator**: A novel simulator that models rigid body dynamics under gravity, contact, and friction forces, enabling accurate computation of object shapes and trajectories. 2. **Surface Points Marching Cubes (SP-MC)**: An efficient algorithm for transforming SDF-based implicit representations into explicit surface points, facilitating differentiable learning with both rendering and physical losses. 3. **Joint Uncertainty Modeling**: Incorporates rendering and physical uncertainties to mitigate inconsistencies in monocular geometric priors and guide pixel sampling, enhancing the reconstruction of thin structures. 4. **Physics-Guided Pixel Sampling**: Adapts per-pixel depth, normal, and instance mask losses based on physical uncertainty, improving the stability and detail of reconstructed objects. Extensive experiments on real and synthetic datasets demonstrate that PHYRECON significantly outperforms state-of-the-art methods in both reconstruction quality and physical plausibility, achieving at least a 40% improvement in stability across all datasets. The method's effectiveness is validated through qualitative and quantitative evaluations, highlighting its potential for physics-demanding applications.PHYRECON is a novel approach that integrates differentiable rendering and physics simulation to learn implicit surface representations for 3D scene reconstruction. The method addresses the limitations of existing methods by incorporating physics constraints, which are often overlooked in neural implicit representations. Key contributions include: 1. **Differentiable Particle-Based Physical Simulator**: A novel simulator that models rigid body dynamics under gravity, contact, and friction forces, enabling accurate computation of object shapes and trajectories. 2. **Surface Points Marching Cubes (SP-MC)**: An efficient algorithm for transforming SDF-based implicit representations into explicit surface points, facilitating differentiable learning with both rendering and physical losses. 3. **Joint Uncertainty Modeling**: Incorporates rendering and physical uncertainties to mitigate inconsistencies in monocular geometric priors and guide pixel sampling, enhancing the reconstruction of thin structures. 4. **Physics-Guided Pixel Sampling**: Adapts per-pixel depth, normal, and instance mask losses based on physical uncertainty, improving the stability and detail of reconstructed objects. Extensive experiments on real and synthetic datasets demonstrate that PHYRECON significantly outperforms state-of-the-art methods in both reconstruction quality and physical plausibility, achieving at least a 40% improvement in stability across all datasets. The method's effectiveness is validated through qualitative and quantitative evaluations, highlighting its potential for physics-demanding applications.