Tutorial on Generalization in Neural Fields, CVPR 2022 Tutorial on Neural Fields in Computer Vision
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Slides for the "generalization" session of our CVPR 2022 tutorial on Neural Fields in Computer Vision. Neural Fields are an emerging technique to parameterize signals that live in spatial coordinates plus time. They parameterize a signal as a continuous function that maps a space-time coordinate to whatever is at that spacetime coordinate - for instance, the geometry of a 3D scene could be encoded in a function that maps a 3D coordinate to whether that coordinate is occupied or not. A neural field parameterizes that function as a neural network. In this session, I gave a high-level overview over how we may use neural fields as the output of a variety of inference algorithms, for instance to reconstruct a complete 3D shape from partial observations in the form of a pointcloud, or to reconstruct a 3D scene from only a single image. You are free to use the slides for any purpose, as long as you keep a note on the slides that acknowledges their source. Neural Fields database: https://neuralfields.cs.brown.edu/ Tutorial website: https://neuralfields.cs.brown.edu/cvpr22
![Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields
Global conditioning
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1[Schmidhuber et al. 1992, Schmidhuber et al. 1993, Stanley et al. 2009, Ha et al., 2016]
Hypernetwork1
Latent code 𝑧](https://image.slidesharecdn.com/cvpr2022neuralfieldstutorialgeneralization-220629174941-2e15bc0f/85/Tutorial-on-Generalization-in-Neural-Fields-CVPR-2022-Tutorial-on-Neural-Fields-in-Computer-Vision-18-320.jpg)
![Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields
From point clouds: Conditioning on Feature Voxel grids
22
Convolutional Occupancy Networks [Peng et al. 2020]
Local Implicit Grid Representations for 3D Scenes [Jiang et al. 2020]
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion [Chabra et al. 2020]
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction [Chibane et al. 2020]](https://image.slidesharecdn.com/cvpr2022neuralfieldstutorialgeneralization-220629174941-2e15bc0f/85/Tutorial-on-Generalization-in-Neural-Fields-CVPR-2022-Tutorial-on-Neural-Fields-in-Computer-Vision-22-320.jpg)
![Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields
From point clouds: Conditioning on Feature Voxel grids
23
Convolutional Occupancy Networks [Peng et al. 2020]
Local Implicit Grid Representations for 3D Scenes [Jiang et al. 2020]
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion [Chabra et al. 2020]
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction [Chibane et al. 2020]](https://image.slidesharecdn.com/cvpr2022neuralfieldstutorialgeneralization-220629174941-2e15bc0f/85/Tutorial-on-Generalization-in-Neural-Fields-CVPR-2022-Tutorial-on-Neural-Fields-in-Computer-Vision-23-320.jpg)
![Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields
From Point clouds: Ground-plan and Tri-plane factorizations
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Convolutional Occupancy Networks [Peng et al. 2020]](https://image.slidesharecdn.com/cvpr2022neuralfieldstutorialgeneralization-220629174941-2e15bc0f/85/Tutorial-on-Generalization-in-Neural-Fields-CVPR-2022-Tutorial-on-Neural-Fields-in-Computer-Vision-25-320.jpg)
![Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields
From Point clouds: Ground-plan and Tri-plane factorizations
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Convolutional Occupancy Networks [Peng et al. 2020]](https://image.slidesharecdn.com/cvpr2022neuralfieldstutorialgeneralization-220629174941-2e15bc0f/85/Tutorial-on-Generalization-in-Neural-Fields-CVPR-2022-Tutorial-on-Neural-Fields-in-Computer-Vision-26-320.jpg)
![Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields
From Point clouds: Ground-plan and Tri-plane factorizations
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Convolutional Occupancy Networks [Peng et al. 2020]](https://image.slidesharecdn.com/cvpr2022neuralfieldstutorialgeneralization-220629174941-2e15bc0f/85/Tutorial-on-Generalization-in-Neural-Fields-CVPR-2022-Tutorial-on-Neural-Fields-in-Computer-Vision-27-320.jpg)
![Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields
: ℝ3 → ℝn
[Adelson et al. 1991, Levoy et al. 1996, Gortler et al. 1996]
Light Field](https://image.slidesharecdn.com/cvpr2022neuralfieldstutorialgeneralization-220629174941-2e15bc0f/85/Tutorial-on-Generalization-in-Neural-Fields-CVPR-2022-Tutorial-on-Neural-Fields-in-Computer-Vision-47-320.jpg)
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Tutorial on Generalization in Neural Fields, CVPR 2022 Tutorial on Neural Fields in Computer Vision
- 1. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields NEURAL FIELDS IN COMPUTER VISION Full-Day Tutorial, June 20th, 2022 neuralfields.cs.brown.edu/cvpr22 Reality Labs Research Yiheng Xie Towaki Takikawa Shunsuke Saito Or Litany James Tompkin Vincent Sitzmann Srinath Sridhar
- 2. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Prior-based Reconstruction of Neural Fields 2 Vincent Sitzmann Assistant Professor, Scene Representation Group www.scenerepresentations.com www.vincentsitzmann.com
- 3. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Motivation: Novel View Synthesis + + Observations Image + Pose & Intrinsics { , , … { Model Novel Views
- 4. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Motivation: Novel View Synthesis 4 Fitting / Optimization Neural Scene Representatio n Neural Renderer
- 5. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Motivation: Novel View Synthesis 5 Inference Neural Scene Representatio n Neural Renderer Inference maps a set of observations to the parameters of a Neural Scene Representation.
- 6. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Overfitting case: Inference = Fitting via Gradient Descent 6 ,… + } { REN D ER 𝜽 SDF + Color MLPs SR N 𝝓 Fitting Rendering Normal map RGB Sitzmann et al: Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations, NeurIPS 2020. min REN D ER 𝜽(SR N 𝝓, 𝜉𝑖) − ℐ𝑖
- 7. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields DeepVoxels, CVPR 2018. NeRF, ECCV 2021 IDR, ECCV 2021 Plenoxels, CVPR 2022 SIREN, NeurIPS 2020
- 8. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields What if we have incomplete observations? 8 REN D ER 𝜽 SDF + Color MLPs SR N 𝝓 Sitzmann et al: Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations, NeurIPS 2020. min REN D ER 𝜽(SR N 𝝓, 𝜉𝑖) − ℐ𝑖 + ℐ, 𝜉 No 3D inform. Normal map RGB
- 9. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Inferring Neural Fields 9 Neural Scene Representatio n Neural Renderer If only a single observation is available, or if only part of the scene has been observed, Inference needs to be prior-based – i.e., we need to learn to reconstruct. ?
- 10. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields General Framework: Encoder-Decoder 10 Neural Scene Representatio n Neural Renderer Decoder Inference Latent Variables {𝑧𝑖}𝑖=1 𝑁 Encoder
- 11. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields What are the latent variables? 11 Neural Scene Representatio n Neural Renderer Inference Encoder Latent Variables {𝑧𝑖}𝑖=1 𝑁
- 12. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields How to predict latent variables from observations? 12 Neural Scene Representatio n Neural Renderer Inference Encoder Latent Variables {𝑧𝑖}𝑖=1 𝑁
- 13. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields How do we decode latent variables into the Neural Field? 13 Neural Scene Representatio n Neural Renderer Inference Encoder Latent Variables {𝑧𝑖}𝑖=1 𝑁
- 14. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields What are the latent variables? 14 Neural Scene Representatio n Neural Renderer Inference Encoder Latent Variables {𝑧𝑖}𝑖=1 𝑁
- 15. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Key Consideration: Locality. 15 Neural Fields in Visual Computing and Beyond, Xie et al., EG STAR 2022 Global Conditioning Local Conditioning
- 16. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Global Latent Codes 16 Neural Fields in Visual Computing and Beyond, Xie et al., EG STAR 2022 Global Conditioning Local Conditioning
- 17. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Global conditioning 17 ? Latent code 𝑧
- 18. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Global conditioning 18 1[Schmidhuber et al. 1992, Schmidhuber et al. 1993, Stanley et al. 2009, Ha et al., 2016] Hypernetwork1 Latent code 𝑧
- 19. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Global Latent Codes: Enables reconstruction from partial observations! 19 Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations, NeurIPS 2019. Differential Volumetric Rendering, Niemeyer et al., CVPR 2020 DeepSDF, Occupancy Networks, IM-Net
- 20. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Global Latent Codes: Enables reconstruction from partial observations! 20 Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations, NeurIPS 2019. Differential Volumetric Rendering, Niemeyer et al., CVPR 2020 DeepSDF, Occupancy Networks, IM-Net Key limitation: Simple, non-compositional scenes. But: Latent Space for full objects (interpolation etc)
- 21. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Local Latent Codes 21 Neural Fields in Visual Computing and Beyond, Xie et al., EG STAR 2022 Global Conditioning Local Conditioning
- 22. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields From point clouds: Conditioning on Feature Voxel grids 22 Convolutional Occupancy Networks [Peng et al. 2020] Local Implicit Grid Representations for 3D Scenes [Jiang et al. 2020] Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion [Chabra et al. 2020] Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction [Chibane et al. 2020]
- 23. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields From point clouds: Conditioning on Feature Voxel grids 23 Convolutional Occupancy Networks [Peng et al. 2020] Local Implicit Grid Representations for 3D Scenes [Jiang et al. 2020] Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion [Chabra et al. 2020] Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction [Chibane et al. 2020]
- 24. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields From point clouds: Conditioning on Feature Voxel grids 24 Generalizes to Compositional Scenes! But: cubic memory complexity :/
- 25. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields From Point clouds: Ground-plan and Tri-plane factorizations 25 Convolutional Occupancy Networks [Peng et al. 2020]
- 26. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields From Point clouds: Ground-plan and Tri-plane factorizations 26 Convolutional Occupancy Networks [Peng et al. 2020]
- 27. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields From Point clouds: Ground-plan and Tri-plane factorizations 27 Convolutional Occupancy Networks [Peng et al. 2020]
- 28. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields From point clouds: Conditioning on Reconstructed Voxelgrids 28 5x less memory!
- 29. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields How to locally condition if sensor domain different than field domain? 29
- 30. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Local Conditioning: Pixel-Aligned Features. 30 PiFU, Saito et al., ICCV 2019. PixelNeRF, Yu et al., CVPR 2021 Grf: Learning a general radiance field…, Trevithick et al.
- 31. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Local Conditioning: Pixel-Aligned Features. 31 PiFU, Saito et al., ICCV 2019. PixelNeRF, Yu et al., CVPR 2021 Grf: Learning a general radiance field…, Trevithick et al. Generalizes much better than global conditioning (like SRNs, DVR). No persistent 3D representation. All priors are learned in image space.
- 32. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Object-centric representations 32 CoLF: Unsupervised Learning of Compositional Object Light Fields, arXiv 2022.
- 33. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Object-centric representations CoLF: Unsupervised Learning of Compositional Object Light Fields, arXiv 2022. uORF, ICLR 2022 Learns to disentangle objects self-supervised. Inference of object-centric latent codes is hard problem. Currently limited to relatively simple scenes, but progress is quick!
- 34. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Conditional Ground Plans for Single-Image 3D Reconstruction 34 Seeing 3D Objects in a Single Image via Self-Supervised Static-Dynamic Disentanglement, Sharma et al. 2022
- 35. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Conditional Ground Plans for Single-Image 3D Reconstruction 35 Seeing 3D Objects in a Single Image via Self-Supervised Static-Dynamic Disentanglement, Sharma et al. 2022
- 36. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Conditional Ground Plans for Single-Image 3D Reconstruction 36 Seeing 3D Objects in a Single Image via Self-Supervised Static-Dynamic Disentanglement, Sharma et al. 2022
- 37. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields How to infer latent codes? 37 Neural Scene Representatio n Neural Renderer Inference Encoder Latent Variables {𝑧𝑖}𝑖=1 𝑁
- 38. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Encoding vs. Auto-Decoding 38 Neural Fields in Visual Computing and Beyond, Xie et al., EG STAR 2022 Encoding Auto-Decoding
- 39. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Auto-Decoding for inverse graphics 39 REN D ER Latent code 𝑧0
- 40. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Auto-Decoding for inverse graphics 40 REN D ER Latent code 𝑧0 𝑧 = arg min 𝑧 REN D ER (Φ) − ℐ 3D-structured, resolution-invariant! Samples need not lie on regular grids!
- 41. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Out-of-distribution generalization 41 3D structure enables generalization to out-of-distribution camera poses! 𝑧 = arg min 𝑧 REN D ER 𝜽(SR N 𝜙=𝐻𝑁𝜓(𝑧), 𝜉) − ℐ Reconstruction CNN encoder Input
- 42. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Other forms of Generalization: Transformer Decoders 42 AIR-Nets, Giebenhain et al. 2022 Scene Representation Transformer Sajjadi et al. 2022
- 43. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Other forms of Generalization: Gradient-based meta-learning Representation In-the loop specialization via gradient descent Meta-Representation 43 MetaSDF: Meta-learning Signed Distance Functions, NeurIPS 2020 Backpropagate through gradient- descent inference at training time. Learn initialization that explains held-out observations when fit to context observation.
- 44. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Inferring Neural Scene Representations 44 Inference Neural Scene Representatio n Neural Renderer Generalization enables reconstruction from incomplete observations. Any other benefits?
- 45. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Problem: Forward map might be expensive! 45 Inference Neural Scene Representatio n Neural Renderer
- 46. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields 3D-structured Neural Scene Representations : ℝ3 → ℝn Hundreds of samples per ray. Time- and memory-intensive training.
- 47. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields : ℝ3 → ℝn [Adelson et al. 1991, Levoy et al. 1996, Gortler et al. 1996] Light Field
- 48. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields : ℝ3 → ℝn Light Field Networks
- 49. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields : ℝ3 → ℝn Light Field Networks
- 50. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Light Field Networks Conditioning Plücker Coords. An Alternative Scene Representation
- 51. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Rendering is learned / representation is “already rendered” 51 Inference Neural Scene Representatio n Neural Renderer
- 52. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Rendering is learned / representation is “already rendered” 52 Inference “Rendered” Neural Scene Representation More difficult inference problem, but more general renderer.
- 53. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Light Field Networks Volumetric Rendering (pixelNeRF) 500 FPS 1 evaluation per ray 0.033 FPS 196 evaluations per ray Real-time. No post-processing, no discrete data structures (octrees, voxelgrids, …). >100x reduction in memory: Can be trained on small GPUs! 15,000x speed 1,000x speed 100x speed 10x speed 1x speed
- 54. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Light Field Networks 500 FPS 1 evaluation per ray
- 55. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Light Fields with Transformers: Scene Representation Transformer (CVPR 2022) No 3D Renderer: Directly parameterizes Light Field!
- 56. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields 56 Things I didn’t talk about ● Generalization in 2D, 1D, etc. neural fields: Images, audio… see LIIF (Chen et al. 2021), … ● Neural field-to-neural field translation, see Spatially-Adaptive Pixelwise Networks for Fast Image Translation (Shaham et al. 2020) ● Generalization for robotics applications (see Neural Descriptor Fields (Simeonov et al.), 3D neural scene … (Li et al., CoRL 2022), Learning Multi-Object Dynamics... (Driess et al. 2022), … ● Generalization for structured field with known a-priori structure (humans, hands, faces, etc)
- 57. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields 57 Outlook ● Generalization gaining traction: Single-scene optimization too limited. ● Opens up completely new ways of thinking about problems: Can amortize otherwise expensive forward maps (light fields). ● Making progress on the question of compositionality w/ object- centric and locally conditioned neural fields. More to come. ● Processing & inferring regular grids is easy. Harder for point clouds / factorized representations, etc. ● Transformers seem to learn a type of local conditioning, but more research necessary.
- 58. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Prior-based Reconstruction of Neural Fields 58 Vincent Sitzmann Assistant Professor, Scene Representation Group www.scenerepresentations.com www.vincentsitzmann.com
- 59. Vincent Sitzmann, CVPR 2022 Tutorial on Neural Fields Q & A Thanks!