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.

翻译：近年来，三维高斯泼溅技术因其能够生成高保真渲染结果而受到关注。与此同时，大多数应用（如游戏、动画和AR/VR）使用基于网格的表示方法来呈现和渲染三维场景。我们提出了一种新颖的方法，将网格表示与三维高斯泼溅相结合，以实现对重建的真实世界场景的高质量渲染。具体而言，我们引入了一种基于距离的高斯泼溅技术，使高斯泼溅与网格表面对齐，并移除对渲染无贡献的冗余高斯泼溅。我们通过计算每个高斯泼溅与网格表面之间的距离来区分紧密绑定和松散绑定的高斯泼溅。紧密绑定的泼溅被展平并与网格几何结构良好对齐。松散绑定的高斯泼溅则用于弥补重建三维网格在渲染过程中产生的伪影。我们提出了一种将高斯泼溅绑定到网格几何结构的训练策略，并同时考虑两种类型的泼溅。在此背景下，我们引入了多种正则化技术，旨在训练过程中使紧密绑定的高斯泼溅精确对齐网格表面。我们在mip-NeRF 360和Deep Blending数据集的大规模无界场景上验证了本方法的有效性。我们的方法超越了近期基于网格的神经渲染技术，实现了PSNR提升2dB，并在基于网格的高斯泼溅方法基础上进一步提升了1.3 dB PSNR（尤其在户外mip-NeRF 360数据集上），展现了更优的渲染质量。我们对各类高斯泼溅进行了分析，与原始三维高斯泼溅技术相比，实现了高斯泼溅数量减少30%。