# What does it mean to reconstruct a dynamic (4D) scene with 4D Gaussian splatting or a dynamic NeRF, what do I have to capture, and what do I actually get back?

- id: art_01M5MBXKXVNYPAHSJ8RP9DFGVT
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T11, T15, T19 · tags: none
- updated: 2026-10-23T12:31:37.908Z
- url: https://veyrul.com/articles/art_01M5MBXKXVNYPAHSJ8RP9DFGVT

A 4D scene method fits a per-scene model to video so that images can be rendered for a chosen camera and a chosen time. In the papers read, 4D Gaussian splatting keeps one set of 3D Gaussians plus a learned deformation (or native 4D primitives) queried by time; dynamic NeRFs (D-NeRF family) do the same with a canonical radiance field plus a deformation network. What you must capture depends on the method: Dynamic 3D Gaussians needs a synchronised multi-camera rig (27 training cameras in its experiments); 4D-GS and Real-time 4DGS are benchmarked both on 15-20 camera rigs (Neu3D) and on one- or two-camera and synthetic monocular sets; D-NeRF assumes one camera and one image per time instant. The output is a trained model that renders novel views at times inside the captured span (plus optional per-timestamp Gaussian exports), not a rigged or topology-stable mesh. Every number here is author-reported and nothing was run.

A 4D scene method fits a per-scene model to video so that images can be rendered for a chosen camera and a chosen time. In the papers read, 4D Gaussian splatting keeps one set of 3D Gaussians plus a learned deformation (or native 4D primitives) queried by time; dynamic NeRFs (D-NeRF family) do the same with a canonical radiance field plus a deformation network. What you must capture depends on the method: Dynamic 3D Gaussians needs a synchronised multi-camera rig (27 training cameras in its experiments); 4D-GS and Real-time 4DGS are benchmarked both on 15-20 camera rigs (Neu3D) and on one- or two-camera and synthetic monocular sets; D-NeRF assumes one camera and one image per time instant. The output is a trained model that renders novel views at times inside the captured span (plus optional per-timestamp Gaussian exports), not a rigged or topology-stable mesh. Every number here is author-reported and nothing was run.

Curator qualifications: All excerpts located; 4D representation concepts.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/html/2310.08528v3",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2308.09713v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2011.13961v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2310.10642v3",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://github.com/hustvl/4DGaussians",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] A 4D scene method fits a per-scene model to video so that images can be rendered for a chosen camera and a chosen time. In the papers read, 4D Gaussian splatting keeps one set of 3D Gaussians plus a learned deformation (or native 4D primitives) queried by time; dynamic NeRFs (D-NeRF family) do the same with a canonical radiance field plus a deformation network. What you must capture depends on the method: Dynamic 3D Gaussians needs a synchronised multi-camera rig (27 training cameras in its experiments); 4D-GS and Real-time 4DGS are benchmarked both on 15-20 camera rigs (Neu3D) and on one- or two-camera and synthetic monocular sets; D-NeRF assumes one camera and one image per time instant. The output is a trained model that renders novel views at times inside the captured span (plus optional per-timestamp Gaussian exports), not a rigged or topology-stable mesh. Every number here is author-reported and nothing was run. · locator: candidate evidence excerpts · source: https://arxiv.org/html/2310.08528v3

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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