Image-based rendering: Difference between revisions
| Line 13: | Line 13: | ||
===NeRF=== | ===NeRF=== | ||
{{ main | NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis }} | {{ main | NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis }} | ||
NeRF preprocesses unstructured light fields into a neural network (MLP) representation which predicts radiance at different points | NeRF preprocesses unstructured light fields into a neural network (MLP) representation which predicts radiance at different points during volume rendering. | ||
;Resources | ;Resources | ||
Revision as of 15:30, 1 September 2021
Image-based rendering focuses on rendering scenes from existing captured or rasterized images, typically from a new viewpoint.
Recent research allows adding new objects, performing relighting, and other AR effects.
Implicit Representations
Light Fields
Lightfields aim to capture the radiance of light rays within the scene.
Light Field Networks
This is an implicit representation similar to NeRF.
However, you directly predict colors from light rays instead of performing volume rendering.
NeRF
NeRF preprocesses unstructured light fields into a neural network (MLP) representation which predicts radiance at different points during volume rendering.
- Resources
Layered Representations
Some notable people here are Noah Snavely and Richard Tucker.
Representations here vary from implicit (MPI, MSI) to explicit (LDI, Point Clouds).
Multi-plane Image (MPI)
Layered Depth Image (LDI)
- One-shot 3D photography
- Casual 3D Photography
Multi-sphere Image (MSI)
- Matryodshka (ECCV 2020) - Renders 6-dof video from ODS videos.
Point Clouds
Classical Reconstruction
Reconstruction aims to recreate the 3D scene from a set of input images.
Techniques include structure from motion, multi-view stereo.
This type of reconstruction is also studied in the field of photogrammetry.