mlsanityData to 7 Oct 2026

DeepVoxels: Learning Persistent 3D Feature Embeddings

arXiv 1812.01024 ↗First posted 3 Dec 2018cs.CVcited by 401

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Abstract (arXiv)

In this work, we address the lack of 3D understanding of generative neural networks by introducing a persistent 3D feature embedding for view synthesis. To this end, we propose DeepVoxels, a learned representation that encodes the view-dependent appearance of a 3D scene without having to explicitly model its geometry. At its core, our approach is based on a Cartesian 3D grid of persistent embedded features that learn to make use of the underlying 3D scene structure. Our approach combines insights from 3D geometric computer vision with recent advances in learning image-to-image mappings based on adversarial loss functions. DeepVoxels is supervised, without requiring a 3D reconstruction of the scene, using a 2D re-rendering loss and enforces perspective and multi-view geometry in a principled manner. We apply our persistent 3D scene representation to the problem of novel view synthesis demonstrating high-quality results for a variety of challenging scenes.

Key facts. Cited by 401 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2018.

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Citing papers per year: 2021: 110; 2022: 77; 2023: 102; 2024: 71; 2025: 34; 2026: 7.

401 papers.

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  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
  3. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
  4. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  5. 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
  6. 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,204
  7. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
  8. 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 904
  9. 2021IBRNet: Learning Multi-View Image-Based Renderingcited by 698
  10. 2021KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPscited by 672
  11. 2023pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstructioncited by 668
  12. 2023HexPlane: A Fast Representation for Dynamic Scenescited by 582
38 more
  1. 2022Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulationcited by 545
  2. 2021Neural Fields in Visual Computing and Beyondcited by 539
  3. 2023DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Visioncited by 514
  4. 2022Point-NeRF: Point-based Neural Radiance Fieldscited by 503
  5. 2024MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motioncited by 431
  6. 2022Decomposing NeRF for Editing via Feature Field Distillationcited by 370
  7. 2021Advances in Neural Renderingcited by 365
  8. 2022Fast Dynamic Radiance Fields with Time-Aware Neural Voxelscited by 344
  9. 2023Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesiscited by 341
  10. 2021PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relightingcited by 332
  11. 2021Light Field Networks: Neural Scene Representations with Single-Evaluation Renderingcited by 330
  12. 2021Neural RGB-D Surface Reconstructioncited by 312
  13. 2022NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstructioncited by 311
  14. 2021Mixture of Volumetric Primitives for Efficient Neural Renderingcited by 277
  15. 20223D Neural Field Generation using Triplane Diffusioncited by 274
  16. 2023Generative Novel View Synthesis with 3D-Aware Diffusion Modelscited by 273
  17. 2021A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Posecited by 233
  18. 2021COIN: COmpression with Implicit Neural representationscited by 232
  19. 2021DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networkscited by 232
  20. 2025SAM 3D: 3Dfy Anything in Imagescited by 230
  21. 2021Learning Object-Compositional Neural Radiance Field for Editable Scene Renderingcited by 228
  22. 2022DynIBaR: Neural Dynamic Image-Based Renderingcited by 224
  23. 2021Editing Conditional Radiance Fieldscited by 222
  24. 2024A Survey on 3D Gaussian Splattingcited by 221
  25. 2023MVImgNet: A Large-scale Dataset of Multi-view Imagescited by 219
  26. 2021GRAM: Generative Radiance Manifolds for 3D-Aware Image Generationcited by 216
  27. 2022NeRF-Editing: Geometry Editing of Neural Radiance Fieldscited by 216
  28. 2021NeX: Real-time View Synthesis with Neural Basis Expansioncited by 215
  29. 2021HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fieldscited by 210
  30. 2021Neural Rays for Occlusion-aware Image-based Renderingcited by 192
  31. 2021Point-Based Neural Rendering with Per-View Optimizationcited by 188
  32. 2021Shape As Points: A Differentiable Poisson Solvercited by 186
  33. 2021CodeNeRF: Disentangled Neural Radiance Fields for Object Categoriescited by 183
  34. 2022SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Viewscited by 175
  35. 2023HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Samplingcited by 173
  36. 2023GauHuman: Articulated Gaussian Splatting from Monocular Human Videoscited by 168
  37. 2022SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Imagecited by 165
  38. 2021Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Renderingcited by 158

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