Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering
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Cited by 25 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Neural rendering methods have significantly advanced photo-realistic 3D scene rendering in various academic and industrial applications. The recent 3D Gaussian Splatting method has achieved the state-of-the-art rendering quality and speed combining the benefits of both primitive-based representations and volumetric representations. However, it often leads to heavily redundant Gaussians that try to fit every training view, neglecting the underlying scene geometry. Consequently, the resulting model becomes less robust to significant view changes, texture-less area and lighting effects. We introduce Scaffold-GS, which uses anchor points to distribute local 3D Gaussians, and predicts their attributes on-the-fly based on viewing direction and distance within the view frustum. Anchor growing and pruning strategies are developed based on the importance of neural Gaussians to reliably improve the scene coverage. We show that our method effectively reduces redundant Gaussians while delivering high-quality rendering. We also demonstrates an enhanced capability to accommodate scenes with varying levels-of-detail and view-dependent observations, without sacrificing the rendering speed.
Key facts. Cited by 695 papers in the map (data through 14 Oct 2026). Builds on 40 of its 57 references that are in the map. First posted on arXiv 30 Nov 2023.
Builds on
40 of 57 references.
References · 40
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
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- 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
- 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
- 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
- 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
- 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,964
- 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
- 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
- 2022TensoRF: Tensorial Radiance Fieldscited by 1,280
- 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,205
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- 2021Volume Rendering of Neural Implicit Surfacescited by 996
- 2020Convolutional Occupancy Networkscited by 761
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- 2022Block-NeRF: Scalable Large Scene Neural View Synthesiscited by 724
- 2023Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fieldscited by 671
- 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 646
- 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 588
- 2022Point-NeRF: Point-based Neural Radiance Fieldscited by 503
- 2021Baking Neural Radiance Fields for Real-Time View Synthesiscited by 468
- 2021Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughscited by 398
- 2021Extracting Triangular 3D Models, Materials, and Lighting From Imagescited by 366
- 2019SynSin: End-to-end View Synthesis from a Single Imagecited by 364
- 2021Light Field Networks: Neural Scene Representations with Single-Evaluation Renderingcited by 332
- 2019Neural Point-Based Graphicscited by 327
- 2022MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architecturescited by 311
- 2021Mixture of Volumetric Primitives for Efficient Neural Renderingcited by 277
- 20223D Neural Field Generation using Triplane Diffusioncited by 274
- 2019Differentiable Surface Splatting for Point-based Geometry Processingcited by 239
- 2021BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Renderingcited by 229
- 2017Learning a Multi-View Stereo Machinecited by 194
- 2021Point-Based Neural Rendering with Per-View Optimizationcited by 190
- 2017Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistencycited by 177
- 2017Learning Efficient Point Cloud Generation for Dense 3D Object Reconstructioncited by 133
- 2023Grid-guided Neural Radiance Fields for Large Urban Scenescited by 100
- 2018Unsupervised Learning of Shape and Pose with Differentiable Point Cloudscited by 89
- 2023VR-NeRF: High-Fidelity Virtualized Walkable Spacescited by 86
- 2023Factor Fields: A Unified Framework for Neural Fields and Beyondcited by 58
- 2023AssetField: Assets Mining and Reconfiguration in Ground Feature Plane Representationcited by 10
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Citing papers per year: 2024: 166; 2025: 289; 2026: 240.
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