Neural Fields

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Revision as of 17:43, 29 March 2023 by David (talk | contribs) (Created page with "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 * [https://arxiv.org/abs...")
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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

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