Textured-GS: Gaussian Splatting with Spatially Defined Color and Opacity
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910696229306368 |
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| author | Huang, Zhentao Gong, Minglun |
| author_facet | Huang, Zhentao Gong, Minglun |
| contents | In this paper, we introduce Textured-GS, an innovative method for rendering Gaussian splatting that incorporates spatially defined color and opacity variations using Spherical Harmonics (SH). This approach enables each Gaussian to exhibit a richer representation by accommodating varying colors and opacities across its surface, significantly enhancing rendering quality compared to traditional methods. To demonstrate the merits of our approach, we have adapted the Mini-Splatting architecture to integrate textured Gaussians without increasing the number of Gaussians. Our experiments across multiple real-world datasets show that Textured-GS consistently outperforms both the baseline Mini-Splatting and standard 3DGS in terms of visual fidelity. The results highlight the potential of Textured-GS to advance Gaussian-based rendering technologies, promising more efficient and high-quality scene reconstructions. Our implementation is available at https://github.com/ZhentaoHuang/Textured-GS. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_09733 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Textured-GS: Gaussian Splatting with Spatially Defined Color and Opacity Huang, Zhentao Gong, Minglun Computer Vision and Pattern Recognition I.4.0 In this paper, we introduce Textured-GS, an innovative method for rendering Gaussian splatting that incorporates spatially defined color and opacity variations using Spherical Harmonics (SH). This approach enables each Gaussian to exhibit a richer representation by accommodating varying colors and opacities across its surface, significantly enhancing rendering quality compared to traditional methods. To demonstrate the merits of our approach, we have adapted the Mini-Splatting architecture to integrate textured Gaussians without increasing the number of Gaussians. Our experiments across multiple real-world datasets show that Textured-GS consistently outperforms both the baseline Mini-Splatting and standard 3DGS in terms of visual fidelity. The results highlight the potential of Textured-GS to advance Gaussian-based rendering technologies, promising more efficient and high-quality scene reconstructions. Our implementation is available at https://github.com/ZhentaoHuang/Textured-GS. |
| title | Textured-GS: Gaussian Splatting with Spatially Defined Color and Opacity |
| topic | Computer Vision and Pattern Recognition I.4.0 |
| url | https://arxiv.org/abs/2407.09733 |