AdaGScale: Viewpoint-Adaptive Gaussian Scaling in 3D Gaussian Splatting to Reduce Gaussian-Tile Pairs
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Abstract (arXiv)
Reducing the number of Gaussian-tile pairs is one of the most promising approaches to improve 3D Gaussian Splatting (3D-GS) rendering speed on GPUs. However, the importance difference existing among Gaussian-tile pairs has never been considered in the previous works. In this paper, we propose AdaGScale, a novel viewpoint-adaptive Gaussian scaling technique for reducing the number of Gaussian-tile pairs. AdaGScale is based on the observation that the peripheral tiles located far from Gaussian center contribute negligibly to pixel color accumulation. This suggests an opportunity for reducing the number of Gaussian-tile pairs based on color contribution. AdaGScale efficiently estimates the color contribution in the peripheral region of each Gaussian during a preprocessing stage and adaptively scales its size based on the peripheral score. As a result, Gaussians with lower importance intersect with fewer tiles during the intersection test, which improves rendering speed while maintaining image quality. The adjusted size is used only for tile intersection test, and the original size is retained during color accumulation to preserve visual fidelity. Experimental results show that AdaGScale achieves a geometric mean speedup of 13.8x over original 3D-GS on a GPU, with only about 0.5 dB degradation in PSNR on city-scale scenes.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 24 references that are in the map. First posted on arXiv 21 Apr 2026.
Builds on
18 of 24 references.
References · 18
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
- 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
- 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
- 2022TensoRF: Tensorial Radiance Fieldscited by 1,280
- 2023Scaffold-GS: Structured 3D Gaussians for View-Adaptive Renderingcited by 695
- 2023LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPScited by 436
- 2021Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughscited by 398
- 2024Mini-Splatting: Representing Scenes with a Constrained Number of Gaussianscited by 213
- 2024CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussianscited by 192
- 2023CompGS: Smaller and Faster Gaussian Splatting with Vector Quantizationcited by 130
- 2021Capturing, Reconstructing, and Simulating: the UrbanScene3D Datasetcited by 118
6 more
- 2024RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPScited by 113
- 2024FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Renderingcited by 88
- 2024LP-3DGS: Learning to Prune 3D Gaussian Splattingcited by 55
- 2024AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radiuscited by 34
- 2024EfficientGS: Streamlining Gaussian Splatting for Large-Scale High-Resolution Scene Representationcited by 27
- 2022Onesweep: A Faster Least Significant Digit Radix Sort for GPUscited by 5
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Citing papers per year: 2026: 1.
1 paper.