u/0x00000032

https://superspl.at/scene/e76ca81c - explosion volume with different count of splats:
- 1M splats
- 100k splats
- 10k splats
- 1k splats
- 100 splats

Free VDB Volume "Gasoline Explosion" from https://jangafx.com/software/embergen/download/free-vdb-animations

Should I add more splats, run more iterations, or stop because the scene has already reached a practical plateau?

The useful part was not the absolute loss value by itself, but the shape of the curve:

• when the run reaches a plateau;
• whether a higher splat budget actually gives a meaningful improvement;
• how much slower training becomes;
• when increasing iterations makes sense;
• where diminishing returns start.

For example, in this scene, the lower-budget runs mostly stopped improving after around 180k–200k iterations, while the 600k splat run still had a curve that looked worth extending.

The article is the main part here — it is more about the workflow and how to interpret the curves than about the tool itself.

I also made a small LichtFeld Studio plugin/repository that exports training metrics to TensorBoard event files and CSV, so different runs can be compared on the same chart.

Article:
https://github.com/Dok11/lichtfeld-tensorboard-export/blob/main/docs/articles/using-loss-curves.en.md

Repository:
https://github.com/Dok11/lichtfeld-tensorboard-export

This is not meant as a universal rule for every scene, but more as a practical way to make training decisions from data instead of guessing.

Interactive Gaussian Splat:
https://superspl.at/scene/85f63eb0

Source videos:
https://www.pexels.com/video/aerial-view-of-historic-church-in-urban-setting-29196253/
https://www.pexels.com/video/aerial-view-of-historic-stone-church-and-neighborhood-29196283/
https://www.pexels.com/video/aerial-view-of-historic-church-and-surrounding-area-29196285/

Location:
https://maps.app.goo.gl/ehEdhmBEXGqDV7JM6

I’ve been experimenting with reconstructing places from video instead of a traditional photo set. For this one, I used footage of Anciolina, extracted 1,180 frames, aligned the cameras in RealityScan, and used that reconstruction as the basis for a Gaussian Splat scene in LichtFeld.

What made this especially interesting to me is that I had already used the same footage once before to build a more traditional 3D model — so this time I wanted to see how the result would feel as a splat instead of a mesh.

Interactive scene: https://superspl.at/scene/1278e99d
Older mesh version: https://sketchfab.com/3d-models/anciolina-3d-hilltop-village-scan-snowy-64ede430838142f5929edfc7eb371efc

The original footage was shot by Mark Soetebier on Pexels, and the creator also has a longer YouTube travel video from the trip.

My workflow:

1. Download video from Pexels
2. Use Blender to convert video to images (1180 frames)
3. Align cameras in RealityScan
4. Export point cloud and camera registrations from RealityScan (manually choosen 249 cams)
5. Create Gaussian Splat scene in LichtFeld

Along the way, I also built a small LichtFeld plugin that exports training metrics to TensorBoard, so I could inspect the reconstruction process in more detail.

I’ll probably write a separate post about that next — what it tracks, why it’s useful, and how it helps with tuning Gaussian Splatting experiments.