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RaDe-GS: Rasterizing Depth in Gaussian Splatting

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arxiv 2406.01467 v2 pith:ASTEW2AU submitted 2024-06-03 cs.GR cs.CV

Baowen Zhang , Chuan Fang , Rakesh Shrestha , Yixun Liang , Xiaoxiao Long , Ping Tan This is my paper · ORCID
classification cs.GRcs.CV
keywords gaussianshapesplattingcomputationalefficiencymethodsaccuracydepth
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 17 Pith papers

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