  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. It achieves a Chamfer
distance error comparable to NeuraLangelo on the DTU dataset and maintains
similar computational efficiency as the original 3D GS methods. Our method is a
significant advancement in Gaussian Splatting and can be directly integrated
into existing Gaussian Splatting-based methods.
