High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by Opacity
Recent advancements in 3D scene representation have underscored the potential of Neural Radiance Fields (NeRFs) for producing high-fidelity renderings of complex scenes. However, NeRFs are hindered by the significant computational burden of volumetric rendering. To address this, 3D Gaussian Splattin...
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/3/1535 |
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| author | Shiyu Qiu Chunlei Wu Zhenghao Wan Siyuan Tong |
| author_facet | Shiyu Qiu Chunlei Wu Zhenghao Wan Siyuan Tong |
| author_sort | Shiyu Qiu |
| collection | DOAJ |
| description | Recent advancements in 3D scene representation have underscored the potential of Neural Radiance Fields (NeRFs) for producing high-fidelity renderings of complex scenes. However, NeRFs are hindered by the significant computational burden of volumetric rendering. To address this, 3D Gaussian Splatting (3DGS) has emerged as an efficient alternative, utilizing Gaussian-based representations and rasterization techniques to achieve faster rendering speeds without sacrificing image quality. Despite these advantages, the large number of Gaussian points and associated internal parameters result in high storage demands. To address this challenge, we propose a pruning strategy applied during the Gaussian densification and pruning phases. Our approach integrates learnable Gaussian masks with a contribution-based pruning mechanism, further enhanced by an opacity update strategy to facilitate the pruning process. This method effectively eliminates redundant Gaussian points and those with minimal contributions to scene construction. Additionally, during the Gaussian parameter compression phase, we employ a combination of teacher–student models and vector quantization to compress the spherical harmonic coefficients. Extensive experimental results demonstrate that our approach reduces the storage requirements of original 3D Gaussian models by over 30 times, with only a minor degradation in rendering quality. |
| format | Article |
| id | doaj-art-0c75ec19f91048478fd79d588b0ffc52 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0c75ec19f91048478fd79d588b0ffc522025-08-20T03:12:34ZengMDPI AGApplied Sciences2076-34172025-02-01153153510.3390/app15031535High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by OpacityShiyu Qiu0Chunlei Wu1Zhenghao Wan2Siyuan Tong3Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266500, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266500, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266500, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266500, ChinaRecent advancements in 3D scene representation have underscored the potential of Neural Radiance Fields (NeRFs) for producing high-fidelity renderings of complex scenes. However, NeRFs are hindered by the significant computational burden of volumetric rendering. To address this, 3D Gaussian Splatting (3DGS) has emerged as an efficient alternative, utilizing Gaussian-based representations and rasterization techniques to achieve faster rendering speeds without sacrificing image quality. Despite these advantages, the large number of Gaussian points and associated internal parameters result in high storage demands. To address this challenge, we propose a pruning strategy applied during the Gaussian densification and pruning phases. Our approach integrates learnable Gaussian masks with a contribution-based pruning mechanism, further enhanced by an opacity update strategy to facilitate the pruning process. This method effectively eliminates redundant Gaussian points and those with minimal contributions to scene construction. Additionally, during the Gaussian parameter compression phase, we employ a combination of teacher–student models and vector quantization to compress the spherical harmonic coefficients. Extensive experimental results demonstrate that our approach reduces the storage requirements of original 3D Gaussian models by over 30 times, with only a minor degradation in rendering quality.https://www.mdpi.com/2076-3417/15/3/15353D gaussian splattingnovel view synthesis3D compression |
| spellingShingle | Shiyu Qiu Chunlei Wu Zhenghao Wan Siyuan Tong High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by Opacity Applied Sciences 3D gaussian splatting novel view synthesis 3D compression |
| title | High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by Opacity |
| title_full | High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by Opacity |
| title_fullStr | High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by Opacity |
| title_full_unstemmed | High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by Opacity |
| title_short | High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by Opacity |
| title_sort | high fold 3d gaussian splatting model pruning method assisted by opacity |
| topic | 3D gaussian splatting novel view synthesis 3D compression |
| url | https://www.mdpi.com/2076-3417/15/3/1535 |
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