Rebuild a 3D scene from a photo set or short video

SAIL-Recon is built for scene reconstruction rather than single-image 3D asset generation. You give it many views of the same place, such as a room walkthrough or an outdoor photo set, and it predicts camera poses, depth, and a point cloud that you can inspect or export. The useful editorial angle is speed and workflow clarity: there is a public Space for a quick proof, plus public weights and demo scripts for running the same reconstruction path locally. A good first test is one coherent sequence from a single scene, then a close look at whether the camera path and coarse geometry stay consistent across the whole capture.

Start with the official Hugging Face Space if you want a fast proof, but use one dense sequence of images or a short walkthrough video instead of unrelated shots. On the first pass, look for three things: whether the recovered camera path feels stable, whether the point cloud preserves the basic layout of the scene, and where the geometry starts to break on reflective, low-texture, or thin structures. If it looks promising, move to the public repo and run `demo.py` locally so you can control the input set and inspect the exported `pred.ply` and `pred.txt` files more reliably.

Treat this as reconstruction, not one-click production geometry. The hosted demo is more of a proof than a polished capture tool, and the practical local path still wants a CUDA-capable GPU plus a coherent multi-view sequence.

• Turn a short phone video or photo set into a rough 3D scene you can inspect quickly.
• Recover camera poses and coarse geometry before a heavier SfM or rendering workflow.
• Check whether a capture sequence is clean enough for localization, mapping, or scene analysis tasks.
• Compare a feed-forward reconstruction workflow against slower optimization-heavy pipelines on your own footage.

Try the demo

View model page