Steepest Descent Density Control for Compact 3D Gaussian Splatting
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arXiv
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| Format: | Preprint |
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2025
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| author | Wang, Peihao Wang, Yuehao Wang, Dilin Mohan, Sreyas Fan, Zhiwen Wu, Lemeng Cai, Ruisi Yeh, Yu-Ying Wang, Zhangyang Liu, Qiang Ranjan, Rakesh |
| author_facet | Wang, Peihao Wang, Yuehao Wang, Dilin Mohan, Sreyas Fan, Zhiwen Wu, Lemeng Cai, Ruisi Yeh, Yu-Ying Wang, Zhangyang Liu, Qiang Ranjan, Rakesh |
| contents | 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time, high-resolution novel view synthesis. By representing scenes as a mixture of Gaussian primitives, 3DGS leverages GPU rasterization pipelines for efficient rendering and reconstruction. To optimize scene coverage and capture fine details, 3DGS employs a densification algorithm to generate additional points. However, this process often leads to redundant point clouds, resulting in excessive memory usage, slower performance, and substantial storage demands - posing significant challenges for deployment on resource-constrained devices. To address this limitation, we propose a theoretical framework that demystifies and improves density control in 3DGS. Our analysis reveals that splitting is crucial for escaping saddle points. Through an optimization-theoretic approach, we establish the necessary conditions for densification, determine the minimal number of offspring Gaussians, identify the optimal parameter update direction, and provide an analytical solution for normalizing off-spring opacity. Building on these insights, we introduce SteepGS, incorporating steepest density control, a principled strategy that minimizes loss while maintaining a compact point cloud. SteepGS achieves a ~50% reduction in Gaussian points without compromising rendering quality, significantly enhancing both efficiency and scalability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05587 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Steepest Descent Density Control for Compact 3D Gaussian Splatting Wang, Peihao Wang, Yuehao Wang, Dilin Mohan, Sreyas Fan, Zhiwen Wu, Lemeng Cai, Ruisi Yeh, Yu-Ying Wang, Zhangyang Liu, Qiang Ranjan, Rakesh Computer Vision and Pattern Recognition 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time, high-resolution novel view synthesis. By representing scenes as a mixture of Gaussian primitives, 3DGS leverages GPU rasterization pipelines for efficient rendering and reconstruction. To optimize scene coverage and capture fine details, 3DGS employs a densification algorithm to generate additional points. However, this process often leads to redundant point clouds, resulting in excessive memory usage, slower performance, and substantial storage demands - posing significant challenges for deployment on resource-constrained devices. To address this limitation, we propose a theoretical framework that demystifies and improves density control in 3DGS. Our analysis reveals that splitting is crucial for escaping saddle points. Through an optimization-theoretic approach, we establish the necessary conditions for densification, determine the minimal number of offspring Gaussians, identify the optimal parameter update direction, and provide an analytical solution for normalizing off-spring opacity. Building on these insights, we introduce SteepGS, incorporating steepest density control, a principled strategy that minimizes loss while maintaining a compact point cloud. SteepGS achieves a ~50% reduction in Gaussian points without compromising rendering quality, significantly enhancing both efficiency and scalability. |
| title | Steepest Descent Density Control for Compact 3D Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.05587 |