Gaussian Splatting (GS) has proven to be highly effective in novel view
synthesis, achieving high-quality and real-time rendering. However, its
potential for reconstructing detailed 3D shapes has not been fully explored.
Existing methods often suffer from limited shape accuracy due to the discrete
and unstructured nature of Gaussian splats, which complicates the shape
extraction. While recent techniques like 2D GS have attempted to improve shape
reconstruction, they often reformulate the Gaussian primitives in ways that
reduce both rendering quality and computational efficiency. To address these
problems, our work introduces a rasterized approach to render the depth maps
and surface normal maps of general 3D Gaussian splats. Our method not only
significantly enhances shape reconstruction accuracy but also maintains the
computational efficiency intrinsic to Gaussian Splatting. Our approach achieves
a Chamfer distance error comparable to NeuraLangelo on the DTU dataset and
similar training and rendering time as traditional Gaussian Splatting on the
Tanks & Temples dataset. Our method is a significant advancement in Gaussian
Splatting and can be directly integrated into existing Gaussian Splatting-based
methods.

高斯飞溅在新视角合成方面已被证明具有很高的效果，但对于重建细节丰富的 3D 形状尚未充分探索。本研究介绍了一种栅格化方法来渲染通用 3D 高斯飞溅的深度图和表面法线图，大大提高了形状重建的准确性，并保持高斯飞溅的计算效率。该方法在 DTU 数据集上达到与 NeuraLangelo 相当的 Chamfer 距离误差，并保持与传统高斯飞溅相似的训练和渲染时间。该方法是高斯飞溅的重要进展，可以直接集成到现有的基于高斯飞溅的方法中。