Improving Densification in 3D Gaussian Splatting for High-Fidelity Rendering
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909739614470144 |
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| author | Deng, Xiaobin Diao, Changyu Li, Min Yu, Ruohan Xu, Duanqing |
| author_facet | Deng, Xiaobin Diao, Changyu Li, Min Yu, Ruohan Xu, Duanqing |
| contents | Although 3D Gaussian Splatting (3DGS) has achieved impressive performance in real-time rendering, its densification strategy often results in suboptimal reconstruction quality. In this work, we present a comprehensive improvement to the densification pipeline of 3DGS from three perspectives: when to densify, how to densify, and how to mitigate overfitting. Specifically, we propose an Edge-Aware Score to effectively select candidate Gaussians for splitting. We further introduce a Long-Axis Split strategy that reduces geometric distortions introduced by clone and split operations. To address overfitting, we design a set of techniques, including Recovery-Aware Pruning, Multi-step Update, and Growth Control. Our method enhances rendering fidelity without introducing additional training or inference overhead, achieving state-of-the-art performance with fewer Gaussians. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12313 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Densification in 3D Gaussian Splatting for High-Fidelity Rendering Deng, Xiaobin Diao, Changyu Li, Min Yu, Ruohan Xu, Duanqing Computer Vision and Pattern Recognition Although 3D Gaussian Splatting (3DGS) has achieved impressive performance in real-time rendering, its densification strategy often results in suboptimal reconstruction quality. In this work, we present a comprehensive improvement to the densification pipeline of 3DGS from three perspectives: when to densify, how to densify, and how to mitigate overfitting. Specifically, we propose an Edge-Aware Score to effectively select candidate Gaussians for splitting. We further introduce a Long-Axis Split strategy that reduces geometric distortions introduced by clone and split operations. To address overfitting, we design a set of techniques, including Recovery-Aware Pruning, Multi-step Update, and Growth Control. Our method enhances rendering fidelity without introducing additional training or inference overhead, achieving state-of-the-art performance with fewer Gaussians. |
| title | Improving Densification in 3D Gaussian Splatting for High-Fidelity Rendering |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.12313 |