mlsanityData to 7 Oct 2026

Bootstrap-GS: Self-Supervised Augmentation for High-Fidelity Gaussian Splatting

arXiv 2404.18669 ↗First posted 29 Apr 2024cs.GR cs.AI cs.CVcited by 0

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Abstract (arXiv)

Recent advancements in 3D Gaussian Splatting (3D-GS) have established new benchmarks for rendering quality and efficiency in 3D reconstruction. However, 3D-GS faces critical limitations when generating novel views that significantly deviate from those encountered during training. Moreover, issues such as dilation and aliasing arise during zoom operations. These challenges stem from a fundamental issue: training sampling deficiency. In this paper, we introduce a bootstrapping framework to address this problem. Our approach synthesizes pseudo-ground truth from novel views that align with the limited training set and reintegrates these synthesized views into the training pipeline. Experimental results demonstrate that our bootstrapping technique not only reduces artifacts but also improves quantitative metrics. Furthermore, our technique is highly adaptable, allowing various Gaussian-based method to benefit from its integration.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 66 of its 91 references that are in the map. First posted on arXiv 29 Apr 2024.

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66 of 91 references.

References · 66

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  8. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  9. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  10. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  11. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  12. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
38 more
  1. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,013
  2. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
  3. 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
  4. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
  5. 20234D Gaussian Splatting for Real-Time Dynamic Scene Renderingcited by 1,144
  6. 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,061
  7. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
  8. 2020Neural Sparse Voxel Fieldscited by 959
  9. 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 904
  10. 2023DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creationcited by 837
  11. 2023Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstructioncited by 753
  12. 2023K-Planes: Explicit Radiance Fields in Space, Time, and Appearancecited by 742
  13. 2023Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesiscited by 731
  14. 2024LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creationcited by 730
  15. 2022Block-NeRF: Scalable Large Scene Neural View Synthesiscited by 720
  16. 2023Scaffold-GS: Structured 3D Gaussians for View-Adaptive Renderingcited by 684
  17. 2023Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fieldscited by 667
  18. 2023Mip-Splatting: Alias-free 3D Gaussian Splattingcited by 661
  19. 2023SyncDreamer: Generating Multiview-consistent Images from a Single-view Imagecited by 638
  20. 2022Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generationcited by 597
  21. 2023HexPlane: A Fast Representation for Dynamic Scenescited by 582
  22. 2022Point-NeRF: Point-based Neural Radiance Fieldscited by 503
  23. 2023Neuralangelo: High-Fidelity Neural Surface Reconstructioncited by 464
  24. 2023LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPScited by 420
  25. 2021Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughscited by 396
  26. 2023Gaussian Grouping: Segment and Edit Anything in 3D Scenescited by 387
  27. 2023SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAMcited by 386
  28. 2023PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamicscited by 368
  29. 2023SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenescited by 339
  30. 2023Text-to-3D using Gaussian Splattingcited by 299
  31. 2023LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matchingcited by 290
  32. 2019What Do Single-view 3D Reconstruction Networks Learn?cited by 259
  33. 2023GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussianscited by 256
  34. 2021BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Renderingcited by 228
  35. 2024Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussianscited by 225
  36. 2020Image Matching across Wide Baselines: From Paper to Practicecited by 224
  37. 2024GaussianPro: 3D Gaussian Splatting with Progressive Propagationcited by 199
  38. 2023MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyondcited by 185

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