Neural Radiance Fields
Reconstructing a 3D scene from photos by training a network to be the scene — a beautiful idea, and it was replaced in three years.
Reviewed July 14, 2026Stable
When not to use it
- NeRF, for anything new. Gaussian Splatting is faster to train, real-time to render, and comparable quality.
- On reflective or transparent surfaces. The representation assumes a point has a colour. A mirror doesn't.
- On dynamic scenes. People, leaves, water — anything that moves breaks it.
- With bad camera poses. This is where projects actually fail, and it looks like the method failed.
Reach for something else instead
- 3D Gaussian Splatting — the successor. Use this.
- Photogrammetry — classical mesh reconstruction. Boring, robust, editable.
- LiDAR scanning — measure it; no reconstruction ambiguity.
- Structure from motion alone — if you want a point cloud, not a renderable scene.
Further reading
- Mildenhall et al. (2020), NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis — the landmark, and positional encoding is the trick.
- Kerbl et al. (2023), 3D Gaussian Splatting for Real-Time Radiance Field Rendering — the replacement, in three years, on rendering speed.
- Tancik et al. (2020), Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains — why coordinate networks need frequency encoding at all.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Starting a new project on NeRF. It was superseded in three years.
- Blaming the method for bad camera poses. COLMAP failing is where most reconstructions die.
- Expecting mirrors to work. The representation can't express "this colour depends on where you stand."
- Missing why Gaussian Splatting won. Rendering speed — an engineering property. The hardware likes rasterising.
At a glance
FieldComputer Vision
The ideatrain a network to be the scene; query a 3D point, get colour and density
The trickpositional encoding, because networks have a spectral bias toward smooth
Replaced by3D Gaussian Splatting, in three years, on rendering speed
The lessona representation the hardware can execute has a structural advantage
DifficultyAdvanced
Often compared with
NeRF vs. Gaussian Splatting — one is an elegant implicit network that takes seconds per frame; the other is a million explicit blobs the GPU rasterises in real time. Three years, and the practical one won.
Where this sits
A destination. 9 concepts lead here, and nothing in the corpus depends on it.
6Levelsteps in
9Needs firstconcepts
0Opens upnothing further
1Areastays here
Learn these firstComputer Vision
Computed from the prerequisite graph, not assigned. How this works