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?
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.
Structured fields
{
"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
Rights
| component | license | hosting | redistribution | modification | training | Evidence and notes |
|---|---|---|---|---|---|---|
| documentation | unknown | permitted | permitted | unknown | unknown | Applies to this original summary only, not to the cited sources. |