RaDe-GS: Rasterizing Depth in Gaussian Splatting

TL;DR

RaDe-GS introduces a rasterized depth and normal rendering pipeline for Gaussian Splatting to improve 3D shape reconstruction without sacrificing the efficiency of GS. It computes depth and Gaussian normals directly in ray space, deriving expressions for depth d and the intersection-based normal using Gaussian centers, covariances, and ray parameters. The approach yields depth maps and surface normal maps from general 3D Gaussian splats, enhancing shape accuracy while preserving rendering speed. On the DTU dataset, it achieves Chamfer distance comparable to NeuraLangelo and maintains similar computational efficiency to standard 3D GS methods, and it can be integrated into existing Gaussian Splatting workflows.

Abstract

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.