Normal-GS: 3D Gaussian Splatting with Normal-Involved Rendering
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Abstract (arXiv)
Rendering and reconstruction are long-standing topics in computer vision and graphics. Achieving both high rendering quality and accurate geometry is a challenge. Recent advancements in 3D Gaussian Splatting (3DGS) have enabled high-fidelity novel view synthesis at real-time speeds. However, the noisy and discrete nature of 3D Gaussian primitives hinders accurate surface estimation. Previous attempts to regularize 3D Gaussian normals often degrade rendering quality due to the fundamental disconnect between normal vectors and the rendering pipeline in 3DGS-based methods. Therefore, we introduce Normal-GS, a novel approach that integrates normal vectors into the 3DGS rendering pipeline. The core idea is to model the interaction between normals and incident lighting using the physically-based rendering equation. Our approach re-parameterizes surface colors as the product of normals and a designed Integrated Directional Illumination Vector (IDIV). To optimize memory usage and simplify optimization, we employ an anchor-based 3DGS to implicitly encode locally-shared IDIVs. Additionally, Normal-GS leverages optimized normals and Integrated Directional Encoding (IDE) to accurately model specular effects, enhancing both rendering quality and surface normal precision. Extensive experiments demonstrate that Normal-GS achieves near state-of-the-art visual quality while obtaining accurate surface normals and preserving real-time rendering performance.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 43 of its 59 references that are in the map. First posted on arXiv 27 Oct 2024.
Builds on
43 of 59 references.
References · 43
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31 more
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Built on by
Citing papers per year: 2024: 2; 2025: 3; 2026: 2.
7 papers.
- 2025Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridgingcited by 99
- 2024Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitivescited by 95
- 2024Surf-NeRF: Surface Regularised Neural Radiance Fieldscited by 3
- 2025UniGS: Unified Geometry-Aware Gaussian Splatting for Multimodal Renderingcited by 2
- 2025Reprojection-Guided 3D Gaussian Splatting Diffusion for Weakly Supervised Single-Image Normal Estimationcited by 0
- 2026Instant Colorization of Gaussian Splatscited by 0
- 2026RelightFormer: Feed-forward Generative Transformer for Multiview Object Relightingcited by 0
- 2026RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting7 Sep 2026
- 2026Instant Colorization of Gaussian Splats18 Apr 2026
- 2025UniGS: Unified Geometry-Aware Gaussian Splatting for Multimodal Rendering14 Oct 2025
- 2025Reprojection-Guided 3D Gaussian Splatting Diffusion for Weakly Supervised Single-Image Normal Estimation8 Aug 2025
- 2025Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging28 Mar 2025
- 2024Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives30 Nov 2024
- 2024Surf-NeRF: Surface Regularised Neural Radiance Fields27 Nov 2024