NeRF vs Gaussian Splatting: What's the Difference?
Guide · 2026-06-09 · 7 min read · by SplatMart Team
NeRF and Gaussian splatting both turn photos into 3D scenes, but they work very differently. We break down speed, quality, editability, and when to use each — in plain English.
NeRF and Gaussian splatting are two ways to turn a set of photos into a photorealistic, explorable 3D scene. The short version: NeRF uses a neural network and produces beautiful results but is usually slow to render, while Gaussian splatting reaches similar quality and renders in real time, which is why it has largely overtaken NeRF for practical use.
Both belong to a family of techniques called radiance fields — they reconstruct not just the shape of a scene but how light leaves every point of it, which is what makes reflections and soft materials look right. Here is how they actually differ.
How NeRF works
NeRF (Neural Radiance Fields) trains a neural network to answer a question: "if I look at this exact point in space from this exact direction, what colour and density do I see?" To render an image, it asks that network thousands of times per pixel and adds the results up. The quality is stunning, but all those network lookups make rendering slow — historically seconds per frame, not real time.
How Gaussian splatting works
Gaussian splatting throws out the neural network. It represents the scene explicitly as millions of little coloured 3D blobs (Gaussians) and draws them directly to the screen, sorted front-to-back. There is no network to query at view time, so a trained splat renders in milliseconds. If you want the full explanation, see our guide to what Gaussian splatting is.
The key differences
Rendering speed
Gaussian splatting wins clearly. It runs in real time on ordinary GPUs and even in a web browser. Classic NeRF is much slower, though faster NeRF variants exist.
Visual quality
Roughly comparable at the high end. Both can look photoreal. NeRF can have an edge on certain volumetric effects like haze; splatting is extremely sharp on solid detail.
Training time
Gaussian splatting usually trains faster — minutes to under an hour — versus longer for many NeRF setups.
Editability
Gaussian splatting is easier to edit. Because the scene is explicit blobs, you can move, crop, recolour, or delete parts of it. A NeRF is locked inside a neural network, which is much harder to edit directly.
File size and portability
Splats are plain data (.ply / .splat) and play in many viewers and engines. NeRFs are tied to their model and tooling, so they are less portable.
Which should you use?
- Choose Gaussian splatting if you need real-time performance, want to use the result in a game engine, VR/AR, or the web, or want to edit the scene. This covers the large majority of practical projects today.
- Consider NeRF mainly for research, or specific volumetric looks where a particular NeRF method excels.
For most creators in 2026, Gaussian splatting is the default choice — and you don't even have to make one yourself. SplatMart sells ready-made, real-time-ready splats you can drop straight into a project.
Frequently asked questions
Is Gaussian splatting better than NeRF?
For most practical uses, yes — it renders in real time, trains faster, and is easier to edit and move between tools, at comparable visual quality. NeRF remains relevant in research and for some volumetric effects.
Is Gaussian splatting a type of NeRF?
Not exactly. They are cousins — both are radiance-field methods that reconstruct a scene from photos — but NeRF uses a neural network while Gaussian splatting uses explicit 3D blobs and no network at render time.
Does Gaussian splatting use AI?
It uses an optimisation process to fit the blobs to your photos, which is a machine-learning technique, but unlike NeRF there is no neural network running when you view the finished scene.
Can I convert a NeRF to a Gaussian splat?
There is no perfect one-click conversion, but since both are built from the same input photos, you can usually retrain the same capture as a Gaussian splat to get a real-time version.