Neural Fields
Neural Fields refers to using neural networks or neural methods to
Techniques
Forward Maps
Forward maps are the differentiable functions which convert the representation to an observed signal.
Shapes
- Occupancy Grids or Voxel Grids
- Signed Distance Functions
- Primary-ray (PRIF)
3D Scenes
- Radiance Fields (NeRF)
- Light Fields
Identity
- Images
Architectures
Neural Networks
- MLP
- CNN + MLP
- Progressive Architectures
Hybrid Representations
- Voxel Grids
These typically combine a octree or voxel grid with an MLP.
Some of these are basically feature grids.
- Neural Sparse Voxel Fields
- KiloNeRF
- Point Clouds
- Mesh
Feature Grids
- Plenoxels
- Plenoctrees
- Hash (Instant-NGP)
- Vector Quantization
https://nv-tlabs.github.io/vqad/
- Factorized Feature Grids
- TensoRF
Generalization
Generalization mainly focuses on learning a prior over the distribution, similar to what existing image generation network do.
This enables tasks such as view synthesis from a single image, shape completion, or inpainting.
- CNN
- pixelNeRF
- Latent Codes
- Hyper Networks
- Light Field Networks
Applications
3D Generation
- EG3D - Adapting Stylegan2, NeRF, and a super-resolution network for generating 3D scenes
- Dream Fields - CLIP-guided NeRF generation
- Dreamfusion - Adapting text-to-image diffusion models to generate NeRFs
Resources
- CVPR 2022 Tutorial on Neural Fields in Computer Vision
- State of the Art on Neural Rendering (Tewari et al., 2020)
- [https://arxiv.org/abs/2111.05849 Advances in Neural Rendering (Tewari et al., 2021)