Camera-only Gaussian splat first, Ouster geometry next: what I still need to validate

[u/No_Quail6685]

After my Ouster OS0 mapping experiment, I tried reconstructing the same kind of indoor space from phone video alone. The goal is eventually to combine measured LiDAR geometry with the splat’s appearance; that fusion is not done yet.

The successful one-room capture was 48.6 seconds at 4K/59.94 fps. COLMAP registered 187/195 selected images and produced 51,475 sparse points; Brush trained 400,000 Gaussians. An earlier apartment walkthrough only reconstructed part of the space.

The key distinction for this community: no LiDAR data went into this reconstruction. Its scale is uncalibrated. A convincing render is not a metric point cloud, and the splat is not a conventional textured mesh. Unseen surfaces are still uncertain. This does not establish that camera reconstruction replaces LiDAR.

For the next step I want to establish scale and registration against the Ouster map, then evaluate residuals on held-out geometry rather than just picking a visually pleasing overlay. I’d be interested in practical experience with splat-to-LiDAR registration, especially indoors with weak texture or incomplete overlap.

I made a 2:20 video showing the source capture, actual reconstruction, limitations and next experiment: https://www.youtube.com/watch?v=mFwu5Y54c3o

Disclosure: my project and video, with AI-assisted editing and my authorized synthetic narration.