Refining Gaussian Splatting: A Volumetric Densification Approach
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909727263293440 |
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| author | Gafoor, Mohamed Abdul Preda, Marius Zaharia, Titus |
| author_facet | Gafoor, Mohamed Abdul Preda, Marius Zaharia, Titus |
| contents | Achieving high-quality novel view synthesis in 3D Gaussian Splatting (3DGS) often depends on effective point primitive management. The underlying Adaptive Density Control (ADC) process addresses this issue by automating densification and pruning. Yet, the vanilla 3DGS densification strategy shows key shortcomings. To address this issue, in this paper we introduce a novel density control method, which exploits the volumes of inertia associated to each Gaussian function to guide the refinement process. Furthermore, we study the effect of both traditional Structure from Motion (SfM) and Deep Image Matching (DIM) methods for point cloud initialization. Extensive experimental evaluations on the Mip-NeRF 360 dataset demonstrate that our approach surpasses 3DGS in reconstruction quality, delivering encouraging performance across diverse scenes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_05187 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Refining Gaussian Splatting: A Volumetric Densification Approach Gafoor, Mohamed Abdul Preda, Marius Zaharia, Titus Graphics Artificial Intelligence Computer Vision and Pattern Recognition Achieving high-quality novel view synthesis in 3D Gaussian Splatting (3DGS) often depends on effective point primitive management. The underlying Adaptive Density Control (ADC) process addresses this issue by automating densification and pruning. Yet, the vanilla 3DGS densification strategy shows key shortcomings. To address this issue, in this paper we introduce a novel density control method, which exploits the volumes of inertia associated to each Gaussian function to guide the refinement process. Furthermore, we study the effect of both traditional Structure from Motion (SfM) and Deep Image Matching (DIM) methods for point cloud initialization. Extensive experimental evaluations on the Mip-NeRF 360 dataset demonstrate that our approach surpasses 3DGS in reconstruction quality, delivering encouraging performance across diverse scenes. |
| title | Refining Gaussian Splatting: A Volumetric Densification Approach |
| topic | Graphics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.05187 |