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.
高斯喷溅（GS）在新视角合成中已被证明非常有效，能够实现高质量和实时渲染。然而，其在重建详细的 3D 形状方面的潜力尚未被充分挖掘。由于高斯喷溅的离散和无结构性质，现有方法常常受到形状精度有限的困扰，这使得形状提取变得复杂。尽管最近的技术如 2D GS 试图改进形状重建，但它们常常重新制定高斯原语，以降低渲染质量和计算效率。为了解决这些问题，我们的工作引入了一种光栅化方法来渲染一般 3D 高斯喷溅的深度图和表面法线图。我们的方法不仅显著提高了形状重建的准确性，还保持了高斯喷溅固有的计算效率。我们的方法在 DTU 数据集上达到了与 NeuraLangelo 相当的 Chamfer 距离误差，并且在 Tanks & Temples 数据集上具有与传统高斯喷溅类似的训练和渲染时间。我们的方法是高斯喷溅的一大进步，并且可以直接集成到现有的基于高斯喷溅的方法中。