mlsanityData to 14 Oct 2026

Frequency-Aware Density Control via Reparameterization for High-Quality Rendering of 3D Gaussian Splatting

arXiv 2503.07000 ↗First posted 10 Mar 2025cs.CVcited by 9

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Abstract (arXiv)

By adaptively controlling the density and generating more Gaussians in regions with high-frequency information, 3D Gaussian Splatting (3DGS) can better represent scene details. From the signal processing perspective, representing details usually needs more Gaussians with relatively smaller scales. However, 3DGS currently lacks an explicit constraint linking the density and scale of 3D Gaussians across the domain, leading to 3DGS using improper-scale Gaussians to express frequency information, resulting in the loss of accuracy. In this paper, we propose to establish a direct relation between density and scale through the reparameterization of the scaling parameters and ensure the consistency between them via explicit constraints (i.e., density responds well to changes in frequency). Furthermore, we develop a frequency-aware density control strategy, consisting of densification and deletion, to improve representation quality with fewer Gaussians. A dynamic threshold encourages densification in high-frequency regions, while a scale-based filter deletes Gaussians with improper scale. Experimental results on various datasets demonstrate that our method outperforms existing state-of-the-art methods quantitatively and qualitatively.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 24 of its 37 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

24 of 37 references.

References · 24

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  2. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  3. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  4. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
  5. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,232
  6. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 861
  7. 2021Depth-supervised NeRF: Fewer Views and Faster Training for Freecited by 735
  8. 2023Scaffold-GS: Structured 3D Gaussians for View-Adaptive Renderingcited by 695
  9. 2021KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPscited by 673
  10. 2023Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fieldscited by 671
  11. 2021FastNeRF: High-Fidelity Neural Rendering at 200FPScited by 511
  12. 2023SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAMcited by 388
12 more
  1. 2023DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenescited by 360
  2. 2022MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architecturescited by 311
  3. 2024GaussianPro: 3D Gaussian Splatting with Progressive Propagationcited by 200
  4. 2021Point-Based Neural Rendering with Per-View Optimizationcited by 190
  5. 2023Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Imagescited by 173
  6. 2024FreGS: 3D Gaussian Splatting with Progressive Frequency Regularizationcited by 105
  7. 2022Differentiable Point-Based Radiance Fields for Efficient View Synthesiscited by 86
  8. 2023VR-NeRF: High-Fidelity Virtualized Walkable Spacescited by 86
  9. 2021FoV-NeRF: Foveated Neural Radiance Fields for Virtual Realitycited by 79
  10. 20243D-HGS: 3D Half-Gaussian Splattingcited by 44
  11. 2024TRIPS: Trilinear Point Splatting for Real-Time Radiance Field Renderingcited by 33
  12. 2021GEASI: Geodesic-based Earliest Activation Sites Identification in cardiac modelscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 6.

9 papers.