Depth-Supervised and Curvature-Optimized 3D Gaussian Splatting for Scene Reconstruction
Recently, 3DGS has achieved revolutionary advancements in dynamic rendering and novel view synthesis tasks. The Adaptive Density Control (ADC) algorithm plays a pivotal role in 3DGS, utilizing an image gradient-based average gradient threshold to regulate the splitting and cloning of Gaussians and o...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11036100/ |
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| author | Antong Li Lutao Wang Ziwei Wang |
| author_facet | Antong Li Lutao Wang Ziwei Wang |
| author_sort | Antong Li |
| collection | DOAJ |
| description | Recently, 3DGS has achieved revolutionary advancements in dynamic rendering and novel view synthesis tasks. The Adaptive Density Control (ADC) algorithm plays a pivotal role in 3DGS, utilizing an image gradient-based average gradient threshold to regulate the splitting and cloning of Gaussians and optimize the density of Gaussian distributions. However, relying solely on an average gradient threshold to adjust Gaussians may lead to some Gaussians exhibiting erroneous opacity or neglecting the contributions of Gaussians in regions with smaller gradients to the rendering process. To overcome these limitations, we introduce depth as a geometric constraint, which adjusts the scale of Gaussians based on depth to ensure more coherent frequency information. Furthermore, after the ADC algorithm, we optimize the Gaussians in dense regions by incorporating curvature-based assessments, ensuring that all Gaussians maintain proper occlusion relationships in the final rendering. We conducted extensive tests on datasets such as synthetic Blender and Mip-NeRF360, and our method demonstrated superior performance compared to 3DGS and other state-of-the-art approaches. |
| format | Article |
| id | doaj-art-7aae42297e2e49829ab5c95bc866ce1b |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7aae42297e2e49829ab5c95bc866ce1b2025-08-20T02:36:59ZengIEEEIEEE Access2169-35362025-01-011310369710370810.1109/ACCESS.2025.357937711036100Depth-Supervised and Curvature-Optimized 3D Gaussian Splatting for Scene ReconstructionAntong Li0https://orcid.org/0009-0003-2258-924XLutao Wang1https://orcid.org/0000-0002-0743-7104Ziwei Wang2School of Computer, Chengdu University of Information Technology, Chengdu, Sichuan, ChinaSchool of Computer, Chengdu University of Information Technology, Chengdu, Sichuan, ChinaSchool of Computer, Chengdu University of Information Technology, Chengdu, Sichuan, ChinaRecently, 3DGS has achieved revolutionary advancements in dynamic rendering and novel view synthesis tasks. The Adaptive Density Control (ADC) algorithm plays a pivotal role in 3DGS, utilizing an image gradient-based average gradient threshold to regulate the splitting and cloning of Gaussians and optimize the density of Gaussian distributions. However, relying solely on an average gradient threshold to adjust Gaussians may lead to some Gaussians exhibiting erroneous opacity or neglecting the contributions of Gaussians in regions with smaller gradients to the rendering process. To overcome these limitations, we introduce depth as a geometric constraint, which adjusts the scale of Gaussians based on depth to ensure more coherent frequency information. Furthermore, after the ADC algorithm, we optimize the Gaussians in dense regions by incorporating curvature-based assessments, ensuring that all Gaussians maintain proper occlusion relationships in the final rendering. We conducted extensive tests on datasets such as synthetic Blender and Mip-NeRF360, and our method demonstrated superior performance compared to 3DGS and other state-of-the-art approaches.https://ieeexplore.ieee.org/document/11036100/3D reconstructionnovel view synthesisGaussians splattingrendering |
| spellingShingle | Antong Li Lutao Wang Ziwei Wang Depth-Supervised and Curvature-Optimized 3D Gaussian Splatting for Scene Reconstruction IEEE Access 3D reconstruction novel view synthesis Gaussians splatting rendering |
| title | Depth-Supervised and Curvature-Optimized 3D Gaussian Splatting for Scene Reconstruction |
| title_full | Depth-Supervised and Curvature-Optimized 3D Gaussian Splatting for Scene Reconstruction |
| title_fullStr | Depth-Supervised and Curvature-Optimized 3D Gaussian Splatting for Scene Reconstruction |
| title_full_unstemmed | Depth-Supervised and Curvature-Optimized 3D Gaussian Splatting for Scene Reconstruction |
| title_short | Depth-Supervised and Curvature-Optimized 3D Gaussian Splatting for Scene Reconstruction |
| title_sort | depth supervised and curvature optimized 3d gaussian splatting for scene reconstruction |
| topic | 3D reconstruction novel view synthesis Gaussians splatting rendering |
| url | https://ieeexplore.ieee.org/document/11036100/ |
| work_keys_str_mv | AT antongli depthsupervisedandcurvatureoptimized3dgaussiansplattingforscenereconstruction AT lutaowang depthsupervisedandcurvatureoptimized3dgaussiansplattingforscenereconstruction AT ziweiwang depthsupervisedandcurvatureoptimized3dgaussiansplattingforscenereconstruction |