Tag: volumetric capture

French Alps — Ghost artifacts on the ski trail

Field notes — Orcières Merlette, Hautes-Alpes, French Alps

→ English below

Les skieurs qui avancent sur la piste créent des artefacts qui révèlent les limites de ce que l’algorithme peut reconstruire à partir d’éléments en mouvement dans le dataset. Capture réalisée à ski, caméra sur perche balancée de gauche à droite, un geste pour élargir le point de vue. Ce mouvement pendulaire introduit une variation latérale dans le dataset, mais aussi une instabilité propre au dispositif : flou de mouvement, changements d’angle rapides, reconstruction partielle des alentours.

Ghost-like traces in the reconstructed scene. Skiers moving ahead on the trail create artifacts that reveal the limits of what the algorithm can reconstruct from moving elements in the dataset. Single-path capture on skis, camera swung left to right on a pole, a gesture to broaden the viewpoint. This pendular motion introduces lateral variation into the dataset, but also instability inherent to the device: motion blur, rapid angle changes, partial reconstruction of the surroundings.

1 — Interactive 3D viewer

Gaussian Splatting — navigable scene (SuperSplat viewer)

2 — Training timelapse

Gaussian Splatting training — 48k iterations (PostShot, × 20 timelapse)

3 — Traveling through the reconstructed scene

Traveling through the reconstructed environment — Unreal Engine, XV3DGS plugin

Training

Method: Gaussian Splatting (PostShot)
Images: 962
Splats: 1.6M
Iterations: 48k
Alignment: RealityCapture — 898 aligned images

Processing

Post-clean: none

Capture

Camera: Insta360 X4
Projection: equirectangular
Capture duration: 2:00 min
Location: Orcières Merlette, Hautes-Alpes, French Alps — outdoor / ski resort

4 — 360° capture excerpt

Source footage — Insta360 X4, equirectangular projection, swing motion, 2:00 min

5 — Scene captures

Ghost artifacts — skiers ahead on the trail | viewpoint-dependent reconstruction

Distant valley — reconstruction instability | out of focus

Radiance field frontier — lateral view

Radiance field — top view, abstract capture

Part of an ongoing research on reconstruction instabilities and viewpoint-dependent artifacts in Gaussian Splatting.

Nicolas Mimault, Bagnolet / Paris · nmimault@gmail.com

March 16, 2026
French Alps — Avatar gameplay & Single-Path Scene Reconstruction

Field notes — Orcières Merlette, Hautes-Alpes, French Alps

→ English below

En se déplaçant dans la scène, l’avatar fait apparaître la structure même de l’archive : les splats se dispersent à son passage, révélant la nature instable et construite de la reconstruction volumétrique, proche d’un souvenir qu’on peine à saisir. Capture réalisée à ski dans un paysage de montagne ouvert, caméra sur perche balancée de gauche à droite — la reconstruction ne capte que ce qu’un seul passage avec la camera 360 en mouvement peut voir.

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As the avatar moves through the scene, it reveals the very structure of the archive: splats scatter at its passage, exposing the unstable and constructed nature of volumetric reconstruction, like a memory we struggle to hold onto. Skiing through an open mountain landscape, camera swung left to right on a pole, the reconstruction captures only what a single moving path can see with a 360 camera.

1 — Avatar interaction & particle forces

Avatar interaction & particle forces — Unreal Engine / TouchDesigner
Music: John Lemke — Vessel

Interaction

Player interaction: Unreal Engine — XV3DGS plugin
Particle system forces controlled via TouchDesigner — MIDI to OSC

Training

Method: Gaussian Splatting (PostShot)
Images: 962
Splats: 1.6M
Training iterations: 48k steps
Alignment: RealityCapture — 898 aligned images

Processing

Post-clean: none

Capture

Camera: Insta360 X4
Projection 360: equirectangular
Capture duration: 2:00 min
Location: Orcières Merlette, Hautes-Alpes, French Alps — outdoor / ski resort

A terrain experiment extending the volumetric research beyond urban spaces — into open mountain landscapes.

Nicolas Mimault, Bagnolet / Paris · nmimault@gmail.com

March 16, 2026
Petite Ceinture — Avatar interaction & local field deformation

Field notes — Petite Ceinture & Bridge, January 2026
Paris, 12e arrondissement — 06.01.2026

→ English below

Tests d’interaction en vue à la troisième personne dans deux environnements Gaussian Splatting reconstruits sous Unreal Engine : la voie ferrée de la Petite Ceinture et le pont adjacent. L’avatar se déplace dans le radiance field — l’espace réagit localement à son passage, produisant des déformations spatiales le long du trajet. Exploration des comportements de forces de particules appliquées à la navigation dans des espaces réels reconstruits.

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Third-person interaction tests inside two Gaussian Splatting reconstructions in Unreal Engine: the Petite Ceinture railway and the adjacent bridge. The avatar navigates through the radiance field — the environment reacts locally to the player’s movement, producing spatial deformations along the path. Particle force behaviors applied to navigation in reconstructed real-world spaces.

1 — Third-person avatar interaction

Avatar interaction & local radiance field deformation — Unreal Engine
Petite Ceinture & Bridge, Paris 12e

Training

Method: Gaussian Splatting (PostShot)
Images: 938 + 625
Splats: 2.0M
Iterations: 150k
Alignment: RealityCapture — 850 + 572 aligned images

Processing

Post-clean: none

Capture

Camera: Insta360 X4
Projection: equirectangular
Location: Petite Ceinture & Bridge, Paris 12e — outdoor / snow

2 — Particle dispersion on avatar passage

Splat dispersion along avatar path — Unreal Engine

3 — Particle dispersion on avatar passage

Splat dispersion along avatar path — Unreal Engine

4 — Particle dispersion on avatar passage

Splat dispersion along avatar path — Unreal Engine

Exploring interactive navigation within the SAMPLE(S) volumetric archive.

Nicolas Mimault, Bagnolet / Paris · nmimault@gmail.com