Reconstructing and Simulating Dynamic 3D Objects with Mesh-adsorbed Gaussian Splatting
(2406.01593)Abstract
3D reconstruction and simulation, while interrelated, have distinct objectives: reconstruction demands a flexible 3D representation adaptable to diverse scenes, whereas simulation requires a structured representation to model motion principles effectively. This paper introduces the Mesh-adsorbed Gaussian Splatting (MaGS) method to resolve such a dilemma. MaGS constrains 3D Gaussians to hover on the mesh surface, creating a mutual-adsorbed mesh-Gaussian 3D representation that combines the rendering flexibility of 3D Gaussians with the spatial coherence of meshes. Leveraging this representation, we introduce a learnable Relative Deformation Field (RDF) to model the relative displacement between the mesh and 3D Gaussians, extending traditional mesh-driven deformation paradigms that only rely on ARAP prior, thus capturing the motion of each 3D Gaussian more precisely. By joint optimizing meshes, 3D Gaussians, and RDF, MaGS achieves both high rendering accuracy and realistic deformation. Extensive experiments on the D-NeRF and NeRF-DS datasets demonstrate that MaGS can generate competitive results in both reconstruction and simulation.
Overview
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The paper introduces Mesh-adsorbed Gaussian Splatting (MaGS), a new method that unifies 3D reconstruction and simulation from monocular video input by integrating 3D Gaussians with a mesh-based approach, significantly improving rendering accuracy and deformation realism.
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The MaGS method features a Mesh-adsorbed 3D Gaussian Representation, constraining 3D Gaussians to hover on the mesh surface, and Relative Deformation Field (RDF) to model precise motion of 3D Gaussians, enabling better coherence of complex deformations.
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Experimental results on D-NeRF and NeRF-DS datasets demonstrated superior performance in terms of PSNR and SSIM, with the method excelling in preserving intricate details and facilitating user-interactive simulations such as mesh dragging.
Overview of the Mesh-adsorbed Gaussian Splatting (MaGS) Method for Dynamic 3D Reconstruction and Simulation
The paper titled "Reconstructing and Simulating Dynamic 3D Objects with Mesh-adsorbed Gaussian Splatting" introduces a novel method, Mesh-adsorbed Gaussian Splatting (MaGS), aimed at addressing the dual challenges of 3D reconstruction and simulation from monocular video input. This framework unifies these two tasks by leveraging a new representation that integrates 3D Gaussians with a mesh-based approach, thereby attaining substantial improvements in rendering accuracy and deformation realism.
Key Contributions
Mesh-adsorbed 3D Gaussian Representation: The MaGS representation constrains 3D Gaussians to hover on the mesh surface rather than being fixed to mesh facets. This design allows the combination of the rendering flexibility of 3D Gaussians with the spatial coherence of meshes, facilitating a unified approach for both reconstruction and simulation tasks.
Relative Deformation Field (RDF): MaGS introduces a learnable RDF that models the relative displacement between the mesh and 3D Gaussians. This extends traditional mesh-driven deformation paradigms, capturing the motion of each 3D Gaussian with greater precision and improving the coherence of complex deformations.
Methodological Insights
Stage I: Mesh Extraction and Deformation Field Estimation
In the initial stage, the approach focuses on coarse shape modeling and deformation estimation. Randomly initialized 3D Gaussians are used to form a deformation field through a multi-layer perceptron (DF-MLP). The optimization of this field enables the system to predict the changes across different frames, forming a basis for the subsequent mesh extraction.
Stage II: Mesh-adsorbed Gaussian Splatting
The second stage involves fine-tuning the representation:
- Mesh-adsorbed Gaussians: Gaussians are initialized on the mesh surface and allowed to hover rather than be rigidly fixed. This flexibility results in better optimization during deformation, preventing artifacts common in other methods.
- Local-Rigid Deformation via ARAP: The use of As-Rigid-As-Possible (ARAP) energy minimization exploits local rigidity, ensuring smooth and realistic mesh deformation.
- 3D Gaussian Splatting for Rendering: Finally, the deformed Mesh-adsorbed Gaussians are rendered using a differentiable renderer, allowing high-quality visualization.
Experimental Evaluation
The method was rigorously tested on the D-NeRF and NeRF-DS datasets, signaling notable advancements over existing techniques. Key metrics used include PSNR, SSIM, MS-SSIM, and LPIPS. The results demonstrated:
- Superior Quantitative Performance: MaGS outperformed existing state-of-the-art methods in terms of PSNR and SSIM across all tested scenes.
- Robust Detail Preservation: The method excelled in maintaining high visual quality by preserving intricate details in dynamic scenarios, thereby significantly reducing floating points and artifacts.
Simulation Capabilities
MaGS also facilitates user-interactive simulations, such as mesh dragging, by directly manipulating the mesh and adjusting the 3D Gaussians accordingly. This feature underscores the practical applicability of MaGS in scenarios requiring real-time and flexible object deformation.
Implications and Future Directions
The MaGS method contributes to both theoretical and practical aspects of computer vision by providing a coherent framework for integrating 3D reconstruction and dynamic simulation. It pushes the boundaries of how we can utilize mesh and point cloud representations in a unified manner to achieve superior rendering and realistic deformation.
Future Developments:
- Enhanced Initial Mesh Quality: Since the success of the MaGS method hinges significantly on the accuracy of the initial mesh, future research could focus on improving mesh extraction techniques, especially for low-resolution inputs.
- Extension to Various Domains: Incremental improvements and adaptations could extend MaGS to applications in virtual reality, medical imaging, and robotics, where accurate and dynamic 3D modeling is crucial.
- Deepfake Mitigation: Given the potential misuse of advanced 3D simulation methods, developing robust detection mechanisms and ethical guidelines will be vital for responsible deployment.
In summation, the Mesh-adsorbed Gaussian Splatting (MaGS) method represents a significant stride in the concurrent fields of 3D reconstruction and simulation, offering a versatile and effective solution backed by solid experimental validation. The adaptability and accuracy of this method highlight its potential broad applicability and room for future enhancements.