Splat Journal

Algorithmic imprints of disappearing spaces

Category: 3D reconstruction & Viewer

3D reconstructions of real-world environments using Gaussian Splatting. Includes capture workflows, data processing and real-time viewers to explore scanned scenes.

  • Port-Blanc — The Rocher du Voleur

    Port-Blanc — The Rocher du Voleur

    Field notes — Port-Blanc, Penvénan, Côtes-d’Armor, Bretagne

    → English below

    Port-Blanc, Penvénan, le Rocher du Voleur.
    Sentier côtier sur la falaise, reconstruit par Gaussian Splatting.

    • Seuls les éléments proches de la trajectoire de capture sont restitués avec précision : la roche, le chemin, l’herbe rase.
    • L’horizon reste flou et instable, totalement dépendant du point de vue du spectateur dans la scène entre ciel, terre et mer.
    • La mer ne peut pas être capturée — constamment en mouvement, elle échappe à l’algorithme qui a besoin de surfaces stables. Elle se dissout dans un ensemble uniforme de splats flous de loin, qui éclate en particules abstraites de près. En dirigeant le regard vers l’eau, la mer devient un champ de particules qui composent une surface que l’algorithme n’a jamais vraiment vue.
    • En changeant la hauteur du point de vue dans la scène, on voit apparaître plusieurs nappes de splats à différentes altitudes. L’algorithme a créé ces layers pour satisfaire différents points de vue du dataset — traces de cette logique de reconstruction par vues.
    • Les images à la limite du radiance field révèlent la structure de la reconstruction : captures de vues en plongée, contre-plongée, depuis les bords de la zone capturée.
    • Il est possible d’avoir une vue “depuis sous l’eau” sans jamais y être entré.

    Se balader dans la reconstruction révèle ses limites.

    Port-Blanc, Penvénan — the Rocher du Voleur.
    Coastal cliff path, reconstructed with Gaussian Splatting.

    • Only elements close to the capture path are rendered with precision: rock, path, sparse grass.
    • The horizon remains blurred and unstable, entirely dependent on the viewer’s point of view within the scene — between sky, land and sea.
    • The sea cannot be captured — constantly in motion, it escapes the algorithm that needs stable surfaces. From a distance it dissolves into a uniform field of blurred splats; up close it bursts into abstract particles. Directing the gaze toward the water, the sea becomes a field of particles composing a surface the algorithm never truly saw.
    • Changing the height of the viewpoint in the scene reveals several layers of splats at different altitudes. The algorithm created these layers to satisfy different points of view from the dataset — traces of this view-based reconstruction logic.
    • Images at the edge of the radiance field reveal the structure of the reconstruction: overhead views, low angles, from the margins of the captured zone.
    • It is possible to have a view “from underwater” without ever entering it.

    Walking through the reconstruction reveals its limits.

    1 — Interactive scene

    Gaussian Splatting — navigable scene (SuperSplat viewer)

    2 — Traveling through the reconstructed scene

    Coastal cliff traveling — Unreal Engine, particle forces via TouchDesigner

    Training

    Method: Gaussian Splatting (PostShot)
    Images: 1,020
    Aligned: 785 (RealityCapture)
    Splats: 1.6M
    Capture duration: 4:47 min
    360 POV frames: 277
    Location: Port-Blanc, Penvénan, Côtes-d’Armor, Bretagne — outdoor / spring

    Interaction

    Player: Unreal Engine
    Particle forces: TouchDesigner — MIDI to OSC

    Processing

    Post-clean: none

    3 — Gaussian Splatting training

    Gaussian Splatting training — 1.6M splats (PostShot)

    4 — Raw 360° capture

    Source footage — Insta360 X4, equirectangular projection, 4:47 min

    5 — Scene captures

    Gaussian Splatting Port-Blanc Brittany — coastal rocks Rocher du Voleur
    Scene capture — coastal rocks, Rocher du Voleur
    Gaussian Splatting Port-Blanc Brittany — rocks and sea viewpoint
    Radiance field frontier — lateral view
    Gaussian Splatting Port-Blanc Brittany — radiance field frontier lateral view
    Gaze toward the sea — surface as splat material
    Gaussian Splatting Port-Blanc Brittany — radiance field top view abstract
    Radiance field — top view, abstract capture
    Gaussian Splatting Port-Blanc Brittany — sea surface as particles
    Abstract capture — lateral view
    Gaussian Splatting Port-Blanc Brittany — abstract capture from underwater viewpoint
    Abstract capture — from “underwater”, out of field

    Part of an ongoing research on navigable volumetric reconstructions and the limits of algorithmic capture.

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

  • French Alps — Ghost artifacts on the ski trail

    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

  • M’Hamid El Ghizlane — Gateway to the Sahara

    M’Hamid El Ghizlane — Gateway to the Sahara

    Field notes — M’Hamid El Ghizlane, Morocco

    1 — Interactive scene

    Gaussian Splatting — navigable scene (SuperSplat viewer)

    → English below

    Végétation éparse, relief aride et dunes environnantes sur de longues distances. La reconstruction peine à restituer la profondeur : les dunes restent floues, l’horizon est difficile à saisir pour l’algorithme.

    Capture réalisée tôt le matin, soleil très bas. La lumière rasante est forte.

    Des splats beige en suspension à 2-3m apparaissent dans la scène: poussière sur la lentille ou surexposition au soleil du désert à confirmer avec l’analyse du dataset.

    La caméra était montée sur perche à 2m50-3m de hauteur. En dessous du sol, un réseau dense de splats se déploie pour tenter de restituer les reliefs sur le sable : l’algorithme construit une profondeur qui n’existe pas dans notre vécu.

    EN

    Sparse vegetation, arid relief and surrounding dunes over long distances. The reconstruction struggles to render depth , dunes remain blurred, the horizon difficult for the algorithm to resolve.

    Captured early in the morning, sun very low, harsh raking light.

    Beige splats suspended at 2-3m appear in the scene , dust on the lens, or overexposure from the desert sun. Camera mounted on a pole at 2.5-3m height.

    Below the ground, a dense network of splats spreads out in an attempt to reconstruct the relief on the sand: the algorithm builds a depth that does not exist in our lived experience.

    Training

    Method: Gaussian Splatting (PostShot)
    Images: 483
    Splats: 1.9M
    Iterations: 35.1k
    Alignment: RealityCapture — 373 aligned images

    Processing

    Post-clean: none

    Capture

    Camera: Insta360 X4
    Projection: equirectangular
    Capture duration: 1 min
    Frame extraction: 1 fps
    Location: M’Hamid El Ghizlane, Morocco — Gateway to the Sahara — outdoor / desert

    2 — Scene captures

    3DGS Morocco - A vast desert landscape featuring gently rolling sand dunes and sparse vegetation under a clear blue sky.
    Scene capture — desert landscape
    3DGS Morocco - A barren desert landscape with sand dunes in the background and several tents on flat ground under a clear blue sky.
    Scene capture — desert camp
    3DGS Morocco - A blurred landscape with a sandy terrain and a clear blue sky, featuring soft clouds and hills in the background.
    Radiance field frontier — lateral view
    3DGS Morocco - A swirling abstract representation of Earth with hues of blue and brown, set against a black background.
    Radiance field — top view, abstract capture
    3DGS Morocco desert — splat network below ground
    Relief reconstruction over long distances
    3DGS Morocco desert — beige splats suspended in scene
    Abstract capture
    3DGS Morocco desert — dune reconstruction instability
    Camp reconstruction — depth instability over long distances
    3DGS Morocco desert — raw dataset equirectangular capture
    Abstract top view – M’Hamid El Ghizlane
    Abstract – Splat network below ground
    Splat network below ground
    Bottom view – Splat network below ground

    A field capture extending the SAMPLE(S) research beyond urban spaces into open desert landscapes.

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

  • Morocco — Reconstruction instabilities & accidental autoportrait

    Morocco — Reconstruction instabilities & accidental autoportrait

    Field notes — Drâa Valley, Morocco

    → English below

    Pour ce rendu, la capture a été limitée à un seul aller-retour dans une ruelle étroite de la Kasbah. Cette contrainte met en évidence les instabilités de reconstruction liées à la qualité de la captation et du choix du dataset. Les artefacts algorithmiques qui apparaissent dépendent du point de vue de l’observateur. Ici, un autoportrait involontaire est reproduit : mon visage émerge dans la scène selon la position de la caméra.

    —

    For this render, the capture was limited to a single back-and-forth path within a narrow Kasbah alley. This constraint highlights reconstruction instabilities linked to capture quality and dataset selection. The algorithmic artifacts that appear are viewpoint-dependent. Here, an unintended self-portrait is reproduced: my face emerges within the scene geometry depending on camera position.

    1 — Interactive scene

    Gaussian Splatting — navigable scene (SuperSplat viewer)

    2 — Traveling environment

    Traveling through the reconstructed environment — Unreal Engine

    Training

    Method: Gaussian Splatting (PostShot)
    Images: 597
    Splats: 2.0M
    Iterations: 60k
    Alignment: RealityCapture — 572 aligned images

    Processing

    Post-clean: none

    Capture

    Camera: Insta360 X4
    Projection: equirectangular
    Capture duration: 1:06 min
    Location: Kasbah Oulad Othmane, Drâa Valley, Morocco — outdoor / desert

    3 — Gaussian Splatting training

    Gaussian Splatting training — 60k iterations | Images: 597 | Splats: 2.0M (PostShot)

    4 — Raw 360° capture

    Source footage — Insta360 X4, equirectangular projection, 1:06 min

    5 — Scene captures

    Gaussian Splatting Kasbah - A blurred landscape featuring a path leading through rugged terrain under a blue sky.
    radiance field frontier
    Gaussian Splatting Kasbah - A person standing in a narrow, rocky pathway surrounded by blurred earthy tones.
    accidental autoportrait
    Gaussian Splatting Kasbah - An abstract representation featuring a blurred blend of blue sky, rocky terrain, and natural elements, creating an impressionistic landscape.
    abstract captures top/bottom view
    Gaussian Splatting Kasbah - Narrow dirt pathway between traditional adobe buildings under a clear blue sky.
    Potshot training capture

    Part of an ongoing research on algorithmic artifacts and the limits of volumetric reproduction.

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

  • Petite Ceinture, under snow — Bridge

    Petite Ceinture, under snow — Bridge

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

    → English below

    Pour ce rendu, j’ai effectué plusieurs itérations afin d’atteindre le résultat attendu au moment de la capture. J’ai observé des artefacts apparaissant dans les zones où des piétons traversaient la scène. 3 tests d’entraînement Gaussian avec différents paramètres et datasets.

    —

    For this render, I went through several iterations in order to reach the result I was expecting at the time of capture. I observed artifacts appearing in areas where pedestrians crossed the scene. 3 Gaussian training tests with different components and datasets.

    1 — Interactive scene

    Gaussian Splatting — navigable scene (SuperSplat viewer)

    2 — Traveling “Pont 1”

    Les artefacts se révèlent là où je croise des piétons
    Artifacts are revealed where I cross into pedestrians
    Traveling through the reconstructed environment — Unreal Engine

    Training

    Method: Gaussian Splatting (PostShot)
    Images: 625
    Splats: 2.0M
    Iterations: 150k
    Alignment: RealityCapture

    Processing

    Post-clean: none

    Capture

    Camera: Insta360 X4
    Projection: equirectangular
    Capture duration: 2:06 min (extract)
    Location: Petite Ceinture, Paris 12e — outdoor / snow

    3 — Training “Pont 1” — iteration 1

    Gaussian Splatting training — 150k iterations | Images: 484 | Splats: 2.0M (PostShot)

    4 — Training “Pont 1” — iteration 2

    Gaussian Splatting training — 100k iterations | Images: 484 | Splats: 2.0M (PostShot)

    5 — Training “Focus Pont” — iteration 3

    Gaussian Splatting training — 150k iterations | Images: 625 | Splats: 2.0M (PostShot)

    6 — Raw 360° capture “Focus Pont”

    Source footage — Insta360 X4, equirectangular projection, 2:36 min

    7 — Raw 360° capture “Bridge 1”

    Source footage — Insta360 X4, equirectangular projection, 2:06 min

    8 — Scene captures

    Gaussian Splatting Petite Ceinture bridge Paris —A snowy urban landscape featuring a bridge overgrown with plants and surrounded by buildings, with graffiti on nearby structures and a serene winter atmosphere.
    Scene capture — bridge focus
    Gaussian Splatting Petite Ceinture bridge Paris — Abstract digital artwork featuring a swirling pattern of light and texture in shades of gray, blue, and silver.
    Radiance field — top view
    Gaussian Splatting Petite Ceinture bridge Paris — A snowy urban landscape with buildings partially obscured by a flurry of snow, showcasing a bridge in the background and a cloudy sky.
    Radiance field frontier — lateral view

    A field iteration of the SAMPLE(S) project — documenting reconstruction instabilities on the Petite Ceinture.

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

  • Petite Ceinture, under snow

    Petite Ceinture, under snow

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

    1 — Interactive scene

    Gaussian Splatting — navigable scene (SuperSplat viewer)

    2 — Traveling

    Traveling through the reconstructed environment — Unreal Engine

    Training

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

    Processing

    Post-clean: none

    Capture

    Camera: Insta360 X4
    Projection: equirectangular
    Capture duration: 2:06 min (extract)
    Location: Petite Ceinture, Paris 12e — outdoor / snow

    3 — Training timelapse

    Gaussian Splatting training — 150k iterations (PostShot, accelerated)

    4 — Raw 360° capture

    Source footage — Insta360 X4, equirectangular projection, 2:06 min

    5 — Scene captures

    Gaussian Splatting Petite Ceinture —A snowy path lined with icy trees, leading towards a distant structure under a clear blue sky.
    Radiance field frontier — lateral view
    Gaussian Splatting Petite Ceinture —An abstract digital artwork featuring swirling shades of blue, white, and hints of light, creating a sense of motion and depth.
    Radiance field — top view, instability zone
    Gaussian Splatting Petite Ceinture —A winter scene showing a snowy railway track surrounded by trees and a bridge in the background.
    Abstract capture — out of field of interest

    Part of the SAMPLE(S) research project — volumetric archive of a disappearing space.

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