3D Gaussian Splatting for Real-Time Radiance Field Rendering
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Cited by 570 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.
Key facts. Cited by 6,141 papers in the map (data through 7 Oct 2026). Builds on 31 of its 60 references that are in the map. First posted on arXiv 8 Aug 2023.
Builds on
31 of 60 references.
References · 31
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
- 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
- 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
- 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
- 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
- 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 904
- 2021KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPscited by 672
- 2021Neural Fields in Visual Computing and Beyondcited by 539
- 2021FastNeRF: High-Fidelity Neural Rendering at 200FPScited by 510
- 2022Point-NeRF: Point-based Neural Radiance Fieldscited by 503
- 2021Baking Neural Radiance Fields for Real-Time View Synthesiscited by 465
- 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
19 more
- 2018DeepVoxels: Learning Persistent 3D Feature Embeddingscited by 401
- 2021Advances in Neural Renderingcited by 365
- 2019SynSin: End-to-end View Synthesis from a Single Imagecited by 364
- 2019Neural Point-Based Graphicscited by 326
- 2021Mixture of Volumetric Primitives for Efficient Neural Renderingcited by 277
- 2019Deferred Neural Rendering: Image Synthesis using Neural Texturescited by 253
- 2019Differentiable Surface Splatting for Point-based Geometry Processingcited by 238
- 2020Free View Synthesiscited by 231
- 2021Point-Based Neural Rendering with Per-View Optimizationcited by 188
- 2015DeepStereo: Learning to Predict New Views from the World's Imagerycited by 185
- 2016View Synthesis by Appearance Flowcited by 172
- 2018Escaping Plato's Cave: 3D Shape From Adversarial Renderingcited by 170
- 2022Variable Bitrate Neural Fieldscited by 116
- 2022Differentiable Point-Based Radiance Fields for Efficient View Synthesiscited by 86
- 2023Neural Point Catacaustics for Novel-View Synthesis of Reflectionscited by 81
- 2016A Versatile Scene Model with Differentiable Visibility Applied to Generative Pose Estimationcited by 42
- 2022VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesiscited by 22
- 2022Software Rasterization of 2 Billion Points in Real Timecited by 13
- 2018Comments on "Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform"cited by 10
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Citing papers per year: 2023: 231; 2024: 1605; 2025: 2315; 2026: 1990.
6,141 papers.
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12 most cited of 19 All 19 (JSON)
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12 newest of 19 All 19 (JSON)
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12 most cited of 5,252 All 5,252 (JSON)
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12 newest of 5,252 All 5,252 (JSON)