High-quality 3D reconstruction is still hard. In particular, with 3DGS, getting full coverage without occlusions is often impractical: training views can look great, but you can barely move around.
With Echo-2, we take a different path: reconstruction as conditional generation.
By leveraging a strong generative prior, Echo-2 creates high-fidelity digital twins from sparse input views—constrained by what was captured, while completing appearance and geometry in a coherent 3D world.
These are a few sample scenes, but the same approach scales to large environments from only a handful of images. API update incoming!
Sparse captures in, complete worlds out.
Echo reconstructs scenes from a small set of views, using world modeling priors to fill what sparse 3DGS leaves behind.
Coming soon to the SpAItial API.
