Welcome to Splat3D
Splat3D turns your photos and videos into interactive, shareable 3D scenes. Upload a video walkthrough, and get back a fully reconstructed 3D Gaussian Splat you can embed, explore, and share with a link.Quickstart
Create your first 3D scene in 5 minutes.
API Reference
Full endpoint documentation with interactive examples.
Quality Presets
Choose the right speed/quality tradeoff for your use case.
Training Parameters
Fine-tune every aspect of the reconstruction pipeline.
Pricing & Credits
Pay-as-you-go credits, the price of a scene at each quality tier, and credit packs.
What Splat3D does
- Upload a video or a zip of photos, optionally with ARKit camera poses from your phone
- Reconstruct camera poses using GPU-accelerated structure-from-motion (GLOMAP)
- Train a 3D Gaussian Splat model using gsplat on L4, A100 or H100 GPUs, by preset
- Compress the result into a web-optimized SOG format for instant loading
- Serve the 3D scene through a shareable link or embed it in your app
Key features
- Configurable quality presets — from fast previews (3K iterations, ~8 min) to ultra quality (30K iterations, ~45 min)
- Full parameter control — override training iterations, resolution, SfM matching, and more
- Multiple input types — video files, photo archives, and ARKit captures from iOS
- Web-optimized output — SOG (compressed Gaussian Splat) format loads instantly in any browser
- Pre-supplied poses — skip structure-from-motion entirely if you already have a COLMAP model
- Quality metrics — get the gaussian count back with every completed scene, and holdout PSNR and SSIM by default on every preset but
fast
Who it’s for
- Developers building 3D experiences into their apps
- Real estate platforms creating virtual walkthroughs
- E-commerce teams building product viewers
- AR/VR creators generating 3D content from real-world captures
- Researchers working with Gaussian Splatting at scale
Base URL
All API requests go to:The API is currently in beta. We’ll do our best not to make breaking changes, but the
advanced training parameters may evolve between versions.