mlsanityData to 7 Oct 2026

Isotropic Gaussian Splatting for Real-Time Radiance Field Rendering

arXiv 2403.14244 ↗First posted 21 Mar 2024cs.CV cs.AI cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

The 3D Gaussian splatting method has drawn a lot of attention, thanks to its high performance in training and high quality of the rendered image. However, it uses anisotropic Gaussian kernels to represent the scene. Although such anisotropic kernels have advantages in representing the geometry, they lead to difficulties in terms of computation, such as splitting or merging two kernels. In this paper, we propose to use isotropic Gaussian kernels to avoid such difficulties in the computation, leading to a higher performance method. The experiments confirm that the proposed method is about bf 100X faster without losing the geometry representation accuracy. The proposed method can be applied in a large range applications where the radiance field is needed, such as 3D reconstruction, view synthesis, and dynamic object modeling.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 21 Mar 2024.

Builds on

8 of 22 references.

References · 8

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2023Mip-Splatting: Alias-free 3D Gaussian Splattingcited by 661
  3. 2023SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Renderingcited by 611
  4. 2022Point-NeRF: Point-based Neural Radiance Fieldscited by 503
  5. 2019Differentiable Surface Splatting for Point-based Geometry Processingcited by 238
  6. 2019Side Window Filteringcited by 27
  7. 2019Weighted Mean Curvaturecited by 15
  8. 2021Fast and High-Quality Blind Multi-Spectral Image Pansharpeningcited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.