DynaSurfGS: Dynamic Surface Reconstruction with Planar-based Gaussian Splatting (2408.13972v1)

Abstract: Dynamic scene reconstruction has garnered significant attention in recent years due to its capabilities in high-quality and real-time rendering. Among various methodologies, constructing a 4D spatial-temporal representation, such as 4D-GS, has gained popularity for its high-quality rendered images. However, these methods often produce suboptimal surfaces, as the discrete 3D Gaussian point clouds fail to align with the object's surface precisely. To address this problem, we propose DynaSurfGS to achieve both photorealistic rendering and high-fidelity surface reconstruction of dynamic scenarios. Specifically, the DynaSurfGS framework first incorporates Gaussian features from 4D neural voxels with the planar-based Gaussian Splatting to facilitate precise surface reconstruction. It leverages normal regularization to enforce the smoothness of the surface of dynamic objects. It also incorporates the as-rigid-as-possible (ARAP) constraint to maintain the approximate rigidity of local neighborhoods of 3D Gaussians between timesteps and ensure that adjacent 3D Gaussians remain closely aligned throughout. Extensive experiments demonstrate that DynaSurfGS surpasses state-of-the-art methods in both high-fidelity surface reconstruction and photorealistic rendering.

• The paper introduces planar-based Gaussian splatting that decomposes 4D voxels into 2D planes for precise dynamic surface reconstruction.
• It employs normal regularization and ARAP constraints to ensure smooth, locally rigid surfaces during object motion.
• Experiments demonstrate improved PSNR, SSIM, and Chamfer Distance performance, setting new benchmarks in rendering and reconstruction.

Dynamic Surface Reconstruction with Planar-based Gaussian Splatting: An Overview

"DynaSurfGS: Dynamic Surface Reconstruction with Planar-based Gaussian Splatting" proposes a novel framework for achieving both photorealistic rendering and high-fidelity surface reconstruction of dynamic scenarios. The DynaSurfGS framework presents a solution to longstanding challenges in dynamic scene reconstruction, particularly the generation of smooth geometric surfaces and the rendering of high-quality images.

Key Contributions and Methods

The authors introduce several innovations within their framework:

1. Integration of Planar-based Gaussian Splatting: Unlike conventional methods which rely on discrete 3D Gaussian point clouds that often misalign with object surfaces, DynaSurfGS employs planar-based Gaussian splatting to enhance surface reconstruction precision. By decomposing 4D voxels into 2D planes, this approach encodes motion and shape changes more accurately.
2. Surface Regularization Techniques:
   • Normal Regularization: This ensures smooth surface reconstruction by integrating unbiased depth rendering to create distance and normal maps. These maps are employed to compute an unbiased depth map, improving surface definition.
   • As-Rigid-As-Possible (ARAP) Constraint: To maintain local rigidity during the object’s motion, ARAP regularization is utilized to keep adjacent 3D Gaussians closely aligned over time.
3. Hex-Plane Representation: This representation facilitates encoding the dynamic spatial-temporal features of scenes. By using a compact MLP to predict Gaussian deformations, DynaSurfGS achieves detailed and consistent dynamic surface reconstructions.

Results

Extensive experiments demonstrate DynaSurfGS’s superiority over state-of-the-art methods in both synthetic datasets (such as D-NeRF and DG-Mesh) and real-world data (Ub4D):

• Image Rendering Quality: The framework outperforms existing models, with a noticeable improvement in PSNR (32.508), SSIM (0.9797), and LPIPS (0.0267). This shows DynaSurfGS’s capability to render photorealistic images while retaining geometric accuracy.
• Surface Reconstruction Accuracy: Quantitative metrics such as Chamfer Distance (CD) and Earth Mover Distance (EMD) indicate that DynaSurfGS delivers smoother and more precise dynamic surfaces compared to its predecessors. For instance, the CD on the DG-Mesh dataset is 0.910, a notable improvement over previous methods.

Implications and Future Directions

The implications of DynaSurfGS are substantial:

• Practical Applications: High-fidelity surface reconstruction with real-time rendering capabilities holds promise for applications in autonomous driving, movie production, and interactive media.
• Theoretical Advances: The integration of planar-based Gaussian splatting with advanced regularization techniques provides a new direction for future research in dynamic scene reconstruction.

Future research could address several remaining challenges:

• Adaptive Regularization: Developing methods to dynamically adjust regularization parameters based on local image features could prevent over-smoothing in some areas.
• Handling Sparse Data: Techniques incorporating large-scale pre-trained models might generate more accurate geometries from limited or missing viewpoints, enhancing overall data reconstruction.

Conclusion

DynaSurfGS represents a significant step forward in dynamic surface reconstruction and rendering. By combining planar-based Gaussian splatting with robust regularization techniques, it sets a new benchmark in both high-quality image rendering and precise surface reconstruction. This framework not only advances the state of the art but also opens new avenues for research and practical implementations in various fields of computer vision and graphics.