PhyRecon: Physically Plausible Neural Scene Reconstruction (2404.16666v4)

Abstract: We address the issue of physical implausibility in multi-view neural reconstruction. While implicit representations have gained popularity in multi-view 3D reconstruction, previous work struggles to yield physically plausible results, limiting their utility in domains requiring rigorous physical accuracy. This lack of plausibility stems from the absence of physics modeling in existing methods and their inability to recover intricate geometrical structures. In this paper, we introduce PHYRECON, the first approach to leverage both differentiable rendering and differentiable physics simulation to learn implicit surface representations. PHYRECON features a novel differentiable particle-based physical simulator built on neural implicit representations. Central to this design is an efficient transformation between SDF-based implicit representations and explicit surface points via our proposed Surface Points Marching Cubes (SP-MC), enabling differentiable learning with both rendering and physical losses. Additionally, PHYRECON models both rendering and physical uncertainty to identify and compensate for inconsistent and inaccurate monocular geometric priors. The physical uncertainty further facilitates physics-guided pixel sampling to enhance the learning of slender structures. By integrating these techniques, our model supports differentiable joint modeling of appearance, geometry, and physics. Extensive experiments demonstrate that PHYRECON significantly improves the reconstruction quality. Our results also exhibit superior physical stability in physical simulators, with at least a 40% improvement across all datasets, paving the way for future physics-based applications.

• The paper presents a novel differentiable physics simulator and the SPMC algorithm that efficiently converts implicit surface representations into explicit points for physical simulation.
• It demonstrates a 40% improvement in physical stability metrics, integrating physical dynamics with neural reconstruction effectively.
• The method addresses rendering and physical uncertainties, paving the way for realistic applications in robotics and animation.

Exploring PhyRecon: Bridging Differentiable Rendering and Physics Simulation for Enhanced 3D Reconstruction

Overview of PhyRecon

PhyRecon introduces a novel approach to reconstruct 3D scenes by integrating differentiable rendering and physics simulations into neural implicit surface representations. This method specifically addresses the challenges of achieving physical plausibility in reconstructed scenes, which has been a significant limitation in prior works largely focused on appearance-based cues. PhyRecon proposes a differentiable particle-based physical simulator coupled with a transformation algorithm, Surface Point Marching Cubes (SPMC), designed to efficiently map implicit surface representations to explicit points suitable for physical interactions.

Methodology and Technical Contributions

PhyRecon's methodology centers on several innovative components:

• Differentiable Physics Simulator: This component accurately computes 3D dynamics such as gravity and friction, integrating these physical aspects into the learning process.
• SPMC (Surface Point Marching Cubes): An effective and efficient algorithm for transforming implicit surface representation (SDF) into a mesh suitable for physics simulations.
• Joint Uncertainty Modeling: Addressing ambiguities in geometric cues by modeling rendering and physical uncertainty. This step helps correct inconsistencies in monocular geometric priors and refines the sampling strategy to enhance the focus on slender structures crucial for physical stability.

Key innovations include:

1. Efficient Transformation Between Representations: The SPMC algorithm allows for a quick and precise conversion between SDF-based representations and explicit surface points, essential for physics-based processing.
2. Enhanced Physics Integration: By outlining a straightforward yet robust method of incorporating physical simulations directly into the learning process of 3D reconstruction.
3. Uncertainty Handling: The dual management of rendering and physical uncertainties aids significantly in honing the reconstruction process, particularly for intricate structures that are ordinarily challenging to capture and stabilize.

Experimental Results

PhyRecon is extensively tested against state-of-the-art methods using standard metrics such as Chamfer Distance (CD), F-Score, Normal Consistency (NC), and Stability Rate (SR). The findings reveal that PhyRecon not only achieves superior reconstruction quality but also substantially enhances physical stability—with at least a 40% improvement across all datasets examined.

Implications and Future Work

The successful implementation of PhyRecon opens new avenues in applications demanding high physical fidelity such as robotics and animated simulations. The method's ability to understand and predict physical interactions within a 3D scene elevates the potential use-cases of neural implicit models.

Going forward, potential enhancements could include:

• Scalability: Optimizing PhyRecon to handle larger and more complex environments.
• Real-Time Application: Reducing computational overhead to facilitate real-time interaction simulations.
• Multi-Material Handling: Extending the model to handle varying material properties, which could further complicate the physics simulation but yield more realistic results.

In summary, PhyRecon sets a new benchmark in integrating physical plausibility with 3D scene reconstruction, providing a robust framework that significantly surpasses existing methods in both generating high-quality visual and physically plausible outcomes. The open-source release of this method is intended to encourage further research and development in this promising intersection of computer vision and physics simulation.