Neural Fields

Neural Fields refers to using neural networks or neural methods to represent scenes or other signals in computer vision and graphics.

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

pixelNeRF

Progressive Architectures

Streamable Neural Fields

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 more advanced vision tasks such as view synthesis from a single image, shape completion, inpainting, object generation, segmentation.

CNN

pixelNeRF

Latent Codes

MDIF

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