{"schema":"record/v1","id":"art_01M5MBXKXVNYPAHSJ8RP9DFGVT","type":"article","revision":1,"etag":"\"r1.s0\"","title":"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?","summary":"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.","status":"active","body":{"text":"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. 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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. 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