Does 3D Gaussian Splatting Need Accurate Volumetric Rendering?
Since its introduction, 3D Gaussian Splatting (3DGS) has become an important reference method for learning 3D representations of a captured scene, allowing real‐time novel‐view synthesis with high visual quality and fast training times. Neural Radiance Fields (NeRFs), which preceded 3DGS, are based...
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| Published in: | Computer graphics forum Vol. 44; no. 2 |
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| Main Authors: | , , , , |
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| Language: | English |
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| Abstract | Since its introduction, 3D Gaussian Splatting (3DGS) has become an important reference method for learning 3D representations of a captured scene, allowing real‐time novel‐view synthesis with high visual quality and fast training times. Neural Radiance Fields (NeRFs), which preceded 3DGS, are based on a principled ray‐marching approach for volumetric rendering. In contrast, while sharing a similar image formation model with NeRF, 3DGS uses a hybrid rendering solution that builds on the strengths of volume rendering and primitive rasterization. A crucial benefit of 3DGS is its performance, achieved through a set of approximations, in many cases with respect to volumetric rendering theory. A naturally arising question is whether replacing these approximations with more principled volumetric rendering solutions can improve the quality of 3DGS. In this paper, we present an in‐depth analysis of the various approximations and assumptions used by the original 3DGS solution. We demonstrate that, while more accurate volumetric rendering can help for low numbers of primitives, the power of efficient optimization and the large number of Gaussians allows 3DGS to outperform volumetric rendering despite its approximations. |
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| AbstractList | Since its introduction, 3D Gaussian Splatting (3DGS) has become an important reference method for learning 3D representations of a captured scene, allowing real‐time novel‐view synthesis with high visual quality and fast training times. Neural Radiance Fields (NeRFs), which preceded 3DGS, are based on a principled ray‐marching approach for volumetric rendering. In contrast, while sharing a similar image formation model with NeRF, 3DGS uses a hybrid rendering solution that builds on the strengths of volume rendering and primitive rasterization. A crucial benefit of 3DGS is its performance, achieved through a set of approximations, in many cases with respect to volumetric rendering theory. A naturally arising question is whether replacing these approximations with more principled volumetric rendering solutions can improve the quality of 3DGS. In this paper, we present an in‐depth analysis of the various approximations and assumptions used by the original 3DGS solution. We demonstrate that, while more accurate volumetric rendering can help for low numbers of primitives, the power of efficient optimization and the large number of Gaussians allows 3DGS to outperform volumetric rendering despite its approximations. Abstract Since its introduction, 3D Gaussian Splatting (3DGS) has become an important reference method for learning 3D representations of a captured scene, allowing real‐time novel‐view synthesis with high visual quality and fast training times. Neural Radiance Fields (NeRFs), which preceded 3DGS, are based on a principled ray‐marching approach for volumetric rendering. In contrast, while sharing a similar image formation model with NeRF, 3DGS uses a hybrid rendering solution that builds on the strengths of volume rendering and primitive rasterization. A crucial benefit of 3DGS is its performance, achieved through a set of approximations, in many cases with respect to volumetric rendering theory. A naturally arising question is whether replacing these approximations with more principled volumetric rendering solutions can improve the quality of 3DGS. In this paper, we present an in‐depth analysis of the various approximations and assumptions used by the original 3DGS solution. We demonstrate that, while more accurate volumetric rendering can help for low numbers of primitives, the power of efficient optimization and the large number of Gaussians allows 3DGS to outperform volumetric rendering despite its approximations. |
| Author | Kerbl, B. Kopanas, G. Drettakis, G. Wimmer, M. Celarek, A. |
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| Cites_doi | 10.1007/978-3-031-72643-9_15 10.1145/3658193 10.1145/3528223.3530127 10.1109/CVPR46437.2021.00149 10.1109/2945.468400 10.1109/CVPR42600.2020.00749 10.1145/3550469.3555413 10.1145/3592426 10.1109/CVPR52688.2022.00539 10.1145/3592433 10.1145/3214834.3214880 10.1109/ACCESS.2024.3408318 10.1109/CVPR52733.2024.00512 10.1145/3687934 10.1109/ICCV48922.2021.00570 10.1109/TVCG.2002.1021576 10.1109/CVPR52733.2024.01839 |
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| Snippet | Since its introduction, 3D Gaussian Splatting (3DGS) has become an important reference method for learning 3D representations of a captured scene, allowing... Abstract Since its introduction, 3D Gaussian Splatting (3DGS) has become an important reference method for learning 3D representations of a captured scene,... |
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| SubjectTerms | Approximation CCS Concepts Computer Science Computing methodologies → Image‐based rendering Rasterization Ray tracing Rendering Volumetric models |
| Title | Does 3D Gaussian Splatting Need Accurate Volumetric Rendering? |
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