Neural Fields: Difference between revisions
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;Factorized Feature Grids | ;Factorized Feature Grids | ||
* TensoRF | * TensoRF | ||
===Dynamic Content=== | |||
====Deformation==== | |||
The idea here is to have an MLP which models the deformation of a canonical frame to the target frame. | |||
* [https://www.albertpumarola.com/research/D-NeRF/index.html D-NeRF (CVPR 2020)] | |||
* [https://hypernerf.github.io/ HyperNeRF (SIGGRAPH Asia 2021)] | |||
====Latent code==== | |||
Use a latent code for each time step. | |||
* [https://neural-3d-video.github.io/ Neural 3D Video Synthesis (CVPR 2022)] | |||
* [https://arxiv.org/abs/2210.15947 NeRFPlayer] | |||
** Use a time-dependent sliding window along the feature channels in a feature grid. | |||
====Time-axis==== | |||
* [https://arxiv.org/abs/2301.11280 Text-To-4D Dynamic Scene Generation (2023)] | |||
** Extends the tri-plane feature grid to a six-plane feature grid ({x, y, z, t} choose 2). | |||
====Segmentation==== | |||
Segment static background and objects from dynamic background and objects | |||
* [https://arxiv.org/abs/2303.03361 NeRFlets (2023)] | |||
* [https://arxiv.org/abs/2303.14536 SUDS: Scalable Urban Dynamic Scenes (2023)] | |||
===Generalization=== | ===Generalization=== | ||
Revision as of 15:13, 30 March 2023
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
- 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
Dynamic Content
Deformation
The idea here is to have an MLP which models the deformation of a canonical frame to the target frame.
Latent code
Use a latent code for each time step.
- Neural 3D Video Synthesis (CVPR 2022)
- NeRFPlayer
- Use a time-dependent sliding window along the feature channels in a feature grid.
Time-axis
- Text-To-4D Dynamic Scene Generation (2023)
- Extends the tri-plane feature grid to a six-plane feature grid ({x, y, z, t} choose 2).
Segmentation
Segment static background and objects from dynamic background and objects
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
- 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