Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration

被引:0
作者
Liang, Zhihao [1 ]
Zhang, Qi [2 ]
Hu, Wenbo [2 ]
Zhu, Lei [3 ]
Feng, Ying [2 ]
Jia, Kui [4 ]
机构
[1] South China Univ Technol, Guangzhou, Peoples R China
[2] Tencent AI Lab, Shenzhen, Peoples R China
[3] City Univ Hong Kong, Hong Kong, Peoples R China
[4] Chinese Univ Hong Kong, Sch Data Sci, Shenzhen, Peoples R China
来源
COMPUTER VISION - ECCV 2024, PT XVII | 2025年 / 15075卷
关键词
3D Gaussian Splatting; Anti-Aliasing; View Synthesis; Cumulative Distribution Function (CDF); Analytic Approximation;
D O I
10.1007/978-3-031-72643-9_17
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
3D Gaussian Splatting (3DGS) recently gained popularity by combining the advantages of both primitive-based and volumetric 3D representations, resulting in improved quality and efficiency for 3D scene rendering. However, 3DGS is not alias-free and still produces severe blurring or jaggies when rendered at varying resolutions because the discrete sampling scheme used treats each pixel as an isolated single point, which is insensitive to changes in the footprints of pixels and is restricted in sampling bandwidth. In this paper, we use a conditioned logistic function as the analytic approximation of the cumulative distribution function (CDF) of the Gaussian signal and calculate the integral by subtracting the CDFs. We introduce this approximation to two-dimensional pixel shading and present Analytic-Splatting, which analytically approximates the Gaussian integral within the 2D-pixel window area to better capture the intensity response of each pixel. Then, we use the approximated response of the pixel window integral area to participate in the transmittance calculation of volume rendering, making Analytic-Splatting sensitive to the changes in pixel footprint at different resolutions. Extensive experiments on various datasets validate that our approach has better anti-aliasing capability that gives more details and better fidelity.
引用
收藏
页码:281 / 297
页数:17
相关论文
共 44 条
[1]  
Akeley K., 1993, Computer Graphics Proceedings, P109, DOI 10.1145/166117.166131
[2]   Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields [J].
Barron, Jonathan T. ;
Mildenhall, Ben ;
Verbin, Dor ;
Srinivasan, Pratul P. ;
Hedman, Peter .
2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2023, :19640-19648
[3]   Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields [J].
Barron, Jonathan T. ;
Mildenhall, Ben ;
Verbin, Dor ;
Srinivasan, Pratul P. ;
Hedman, Peter .
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022), 2022, :5460-5469
[4]   Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields [J].
Barron, Jonathan T. ;
Mildenhall, Ben ;
Tancik, Matthew ;
Hedman, Peter ;
Martin-Brualla, Ricardo ;
Srinivasan, Pratul P. .
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021), 2021, :5835-5844
[5]   A logistic approximation to the cumulative normal distribution [J].
Bowling, Shannon R. ;
Khasawneh, Mohammad T. ;
Kaewkuekool, Sittichai ;
Cho, Byung Rae .
JOURNAL OF INDUSTRIAL ENGINEERING AND MANAGEMENT-JIEM, 2009, 2 (01) :114-127
[6]   TensoRF: Tensorial Radiance Fields [J].
Chen, Anpei ;
Xu, Zexiang ;
Geiger, Andreas ;
Yu, Jingyi ;
Su, Hao .
COMPUTER VISION - ECCV 2022, PT XXXII, 2022, 13692 :333-350
[7]  
Chen HL, 2025, Arxiv, DOI arXiv:2312.00846
[8]  
Feng YT, 2024, Arxiv, DOI arXiv:2401.15318
[9]   Plenoxels: Radiance Fields without Neural Networks [J].
Fridovich-Keil, Sara ;
Yu, Alex ;
Tancik, Matthew ;
Chen, Qinhong ;
Recht, Benjamin ;
Kanazawa, Angjoo .
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022), 2022, :5491-5500
[10]   Strivec: Sparse Tri-Vector Radiance Fields [J].
Gao, Quankai ;
Xu, Qiangeng ;
Su, Hao ;
Neumann, Ulrich ;
Xu, Zexiang .
2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2023, :17523-17533