GaussianPOP: Principled Simplification Framework for Compact 3D Gaussian Splatting via Error Quantification
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908818033606656 |
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| author | Lee, Soonbin Kim, Yeong-Gyu Sasse, Simon Borges, Tomas M. Sanchez, Yago Ryu, Eun-Seok Schierl, Thomas Hellge, Cornelius |
| author_facet | Lee, Soonbin Kim, Yeong-Gyu Sasse, Simon Borges, Tomas M. Sanchez, Yago Ryu, Eun-Seok Schierl, Thomas Hellge, Cornelius |
| contents | Existing 3D Gaussian Splatting simplification methods commonly use importance scores, such as blending weights or sensitivity, to identify redundant Gaussians. However, these scores are not driven by visual error metrics, often leading to suboptimal trade-offs between compactness and rendering fidelity. We present GaussianPOP, a principled simplification framework based on analytical Gaussian error quantification. Our key contribution is a novel error criterion, derived directly from the 3DGS rendering equation, that precisely measures each Gaussian's contribution to the rendered image. By introducing a highly efficient algorithm, our framework enables practical error calculation in a single forward pass. The framework is both accurate and flexible, supporting on-training pruning as well as post-training simplification via iterative error re-quantification for improved stability. Experimental results show that our method consistently outperforms existing state-of-the-art pruning methods across both application scenarios, achieving a superior trade-off between model compactness and high rendering quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06830 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GaussianPOP: Principled Simplification Framework for Compact 3D Gaussian Splatting via Error Quantification Lee, Soonbin Kim, Yeong-Gyu Sasse, Simon Borges, Tomas M. Sanchez, Yago Ryu, Eun-Seok Schierl, Thomas Hellge, Cornelius Computer Vision and Pattern Recognition Existing 3D Gaussian Splatting simplification methods commonly use importance scores, such as blending weights or sensitivity, to identify redundant Gaussians. However, these scores are not driven by visual error metrics, often leading to suboptimal trade-offs between compactness and rendering fidelity. We present GaussianPOP, a principled simplification framework based on analytical Gaussian error quantification. Our key contribution is a novel error criterion, derived directly from the 3DGS rendering equation, that precisely measures each Gaussian's contribution to the rendered image. By introducing a highly efficient algorithm, our framework enables practical error calculation in a single forward pass. The framework is both accurate and flexible, supporting on-training pruning as well as post-training simplification via iterative error re-quantification for improved stability. Experimental results show that our method consistently outperforms existing state-of-the-art pruning methods across both application scenarios, achieving a superior trade-off between model compactness and high rendering quality. |
| title | GaussianPOP: Principled Simplification Framework for Compact 3D Gaussian Splatting via Error Quantification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.06830 |