mlsanityData to 7 Oct 2026

BerfScene: Bev-conditioned Equivariant Radiance Fields for Infinite 3D Scene Generation

arXiv 2312.02136 ↗First posted 4 Dec 2023cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

Generating large-scale 3D scenes cannot simply apply existing 3D object synthesis technique since 3D scenes usually hold complex spatial configurations and consist of a number of objects at varying scales. We thus propose a practical and efficient 3D representation that incorporates an equivariant radiance field with the guidance of a bird's-eye view (BEV) map. Concretely, objects of synthesized 3D scenes could be easily manipulated through steering the corresponding BEV maps. Moreover, by adequately incorporating positional encoding and low-pass filters into the generator, the representation becomes equivariant to the given BEV map. Such equivariance allows us to produce large-scale, even infinite-scale, 3D scenes via synthesizing local scenes and then stitching them with smooth consistency. Extensive experiments on 3D scene datasets demonstrate the effectiveness of our approach. Our project website is at https://zqh0253.github.io/BerfScene/.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 53 of its 63 references that are in the map. First posted on arXiv 4 Dec 2023.

Builds on

53 of 63 references.

References · 53

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  3. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  4. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  5. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  6. 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,483
  7. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,445
  8. 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,204
  9. 2021Alias-Free Generative Adversarial Networkscited by 1,181
  10. 2020GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fieldscited by 784
  11. 2020pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesiscited by 708
  12. 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 694
38 more
  1. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 688
  2. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 555
  3. 2021StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesiscited by 477
  4. 2022GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Imagescited by 446
  5. 2019Making Convolutional Networks Shift-Invariant Againcited by 390
  6. 2023Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Modelcited by 376
  7. 20203D-FRONT: 3D Furnished Rooms with layOuts and semaNTicscited by 375
  8. 2018Image Generation from Scene Graphscited by 324
  9. 20203D-FUTURE: 3D Furniture shape with TextUREcited by 306
  10. 2020Neural Scene Graphs for Dynamic Scenescited by 298
  11. 2021StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generationcited by 289
  12. 20223D Neural Field Generation using Triplane Diffusioncited by 274
  13. 2023Generative Novel View Synthesis with 3D-Aware Diffusion Modelscited by 273
  14. 2021GRAM: Generative Radiance Manifolds for 3D-Aware Image Generationcited by 216
  15. 2023DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Modelcited by 213
  16. 20233DGen: Triplane Latent Diffusion for Textured Mesh Generationcited by 181
  17. 2023NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusioncited by 179
  18. 2020How Much Position Information Do Convolutional Neural Networks Encode?cited by 171
  19. 2020BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Imagescited by 166
  20. 2020On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Locationcited by 141
  21. 2022VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Gridscited by 133
  22. 2021Unconstrained Scene Generation with Locally Conditioned Radiance Fieldscited by 130
  23. 2018Visual Object Networks: Image Generation with Disentangled 3D Representationcited by 124
  24. 20213D-aware Image Synthesis via Learning Structural and Textural Representationscited by 109
  25. 2022EpiGRAF: Rethinking training of 3D GANscited by 108
  26. 2022GIRAFFE HD: A High-Resolution 3D-aware Generative Modelcited by 86
  27. 2023NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Modelscited by 76
  28. 2021A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesiscited by 72
  29. 2023SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collectionscited by 72
  30. 2020Positional Encoding as Spatial Inductive Bias in GANscited by 69
  31. 2023InfiniCity: Infinite-Scale City Synthesiscited by 68
  32. 2022DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesiscited by 61
  33. 2023CC3D: Layout-Conditioned Generation of Compositional 3D Scenescited by 58
  34. 2021Generative Occupancy Fields for 3D Surface-Aware Image Synthesiscited by 51
  35. 2023AutoDecoding Latent 3D Diffusion Modelscited by 50
  36. 20233D generation on ImageNetcited by 47
  37. 2022Disentangled3D: Learning a 3D Generative Model with Disentangled Geometry and Appearance from Monocular Imagescited by 39
  38. 20223D-Aware Indoor Scene Synthesis with Depth Priorscited by 32

50 most cited of 53 All 53 (JSON)

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2023SceneWiz3D: Towards Text-guided 3D Scene Compositioncited by 54
  2. 2024Urban Scene Diffusion through Semantic Occupancy Mapcited by 14
  3. 2025DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generationcited by 1
  4. 2025NeoWorld: Neural Simulation of Explorable Virtual Worlds via Progressive 3D Unfoldingcited by 1