MeshGS: Adaptive Mesh-Aligned Gaussian Splatting for High-Quality Rendering (2410.08941v1)

Abstract: Recently, 3D Gaussian splatting has gained attention for its capability to generate high-fidelity rendering results. At the same time, most applications such as games, animation, and AR/VR use mesh-based representations to represent and render 3D scenes. We propose a novel approach that integrates mesh representation with 3D Gaussian splats to perform high-quality rendering of reconstructed real-world scenes. In particular, we introduce a distance-based Gaussian splatting technique to align the Gaussian splats with the mesh surface and remove redundant Gaussian splats that do not contribute to the rendering. We consider the distance between each Gaussian splat and the mesh surface to distinguish between tightly-bound and loosely-bound Gaussian splats. The tightly-bound splats are flattened and aligned well with the mesh geometry. The loosely-bound Gaussian splats are used to account for the artifacts in reconstructed 3D meshes in terms of rendering. We present a training strategy of binding Gaussian splats to the mesh geometry, and take into account both types of splats. In this context, we introduce several regularization techniques aimed at precisely aligning tightly-bound Gaussian splats with the mesh surface during the training process. We validate the effectiveness of our method on large and unbounded scene from mip-NeRF 360 and Deep Blending datasets. Our method surpasses recent mesh-based neural rendering techniques by achieving a 2dB higher PSNR, and outperforms mesh-based Gaussian splatting methods by 1.3 dB PSNR, particularly on the outdoor mip-NeRF 360 dataset, demonstrating better rendering quality. We provide analyses for each type of Gaussian splat and achieve a reduction in the number of Gaussian splats by 30% compared to the original 3D Gaussian splatting.

• The paper introduces MeshGS, a novel adaptive method that aligns Gaussian splats with mesh surfaces to mitigate rendering artifacts.
• It employs a distance-based approach to differentiate tightly-bound and loosely-bound splats, refining scene fidelity.
• The method achieves a 2dB PSNR improvement and a 30% reduction in splat count, enhancing both quality and efficiency in 3D rendering.

MeshGS: Adaptive Mesh-Aligned Gaussian Splatting for High-Quality Rendering

The paper "MeshGS: Adaptive Mesh-Aligned Gaussian Splatting for High-Quality Rendering" introduces a novel approach aimed at integrating mesh representations with 3D Gaussian splats to facilitate high-quality rendering of reconstructed real-world scenes. The authors propose a method that adapts Gaussian splats to align with mesh surfaces, removing redundant splats and focusing on enhancing rendering fidelity by addressing mesh artifacts, a persistent issue in complex scene rendering.

Methodology

The authors develop a distance-based Gaussian splatting technique that differentiates between tightly-bound and loosely-bound Gaussian splats based on their proximity to the mesh surface. Tightly-bound splats are aligned and flattened against the mesh, while loosely-bound splats are utilized to compensate for artifacts arising from mesh reconstruction inaccuracies. This distinction allows for a more precise rendering that accommodates areas where the mesh may inherently fail to provide an accurate representation of the scene's geometry.

The method also incorporates regularization strategies during training to ensure that Gaussian splats align accurately with mesh surfaces. This is critical in large-scale, unbounded scene reconstruction, where the complexity of geometrical elements and details pose substantial challenges.

Results

The performance of MeshGS is validated using datasets such as mip-NeRF 360 and Deep Blending. The authors report that their method achieves a 2dB increase in PSNR over state-of-the-art mesh-based neural rendering techniques and a 1.3dB improvement over mesh-based Gaussian splatting methods, on the mip-NeRF 360 dataset. Moreover, the approach reduces the number of Gaussian splats by 30% compared to the baseline 3D Gaussian splatting.

Such results underscore the efficacy of combining mesh-based structures with adaptive Gaussian splats in improving rendering quality, particularly in scenes with complex and detailed geometrical elements.

Implications and Future Work

This research has both practical and theoretical implications. Practically, by enhancing rendering quality and efficiency, the method supports potential integrations into workflow pipelines used in gaming, animation, and AR/VR applications. Theoretically, it advances the understanding of how mesh representations and Gaussian splats can be synergistically utilized for improved scene representation.

Future work could focus on refining the approach to accommodate even more complex scenes and further reduce computation overhead. The exploration of alternative strategies for initializing and training splats using diverse datasets might also yield insights, especially in scenarios where mesh quality varies significantly.

In summary, the paper presents a significant contribution to the domain of high-quality rendering, cleverly combining mesh-based representations with Gaussian splatting to address previously unmet challenges in the representation of intricate scenes.