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

Zhihao Liang, Qi Zhang*, Wenbo Hu, Lei Zhu, Ying Feng, Kui Jia*

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2024
Subtitle of host publication18th European Conference, Proceedings, Part XVII
EditorsAleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol
PublisherSpringer, Cham
Pages281-297
Edition1
ISBN (Electronic)978-3-031-72643-9
ISBN (Print)978-3-031-72642-2
DOIs
Publication statusPublished - 2024
Event18th European Conference on Computer Vision (ECCV 2024) - MiCo Milano, Milan, Italy
Duration: 29 Sept 20244 Oct 2024
https://eccv.ecva.net/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15075 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th European Conference on Computer Vision (ECCV 2024)
Abbreviated titleECCV2024
PlaceItaly
CityMilan
Period29/09/244/10/24
Internet address

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Research Keywords

  • 3D Gaussian Splatting
  • Analytic Approximation
  • Anti-Aliasing
  • Cumulative Distribution Function (CDF)
  • View Synthesis

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