Nvidia researchers have built Déjà View, a 117 million-parameter AI model that turns photos taken from different angles into 3D scenes and matches or beats models up to 10 times its size by reusing a single layer over and over.
Key Takeaways
- Déjà View turns multi-angle photos into 3D geometry and camera positions with just 117 million parameters
- It matches or beats models with 8 to 10 times more parameters on five 3D reconstruction benchmarks
- It also uses 1.9 to 2.3 times less computing power than the largest rivals it was tested against
- The paper is an oral presentation at NeurIPS 2026, and Nvidia has released the code
Nvidia's AI research team shared the model on Wednesday. The work comes from 12 researchers at Nvidia, ETH Zürich, the University of Toronto and the University of Modena and Reggio Emilia.
How One Layer Does The Work Of Many
Most 3D reconstruction models have grown past 1 billion parameters by stacking many different layers. Déjà View instead runs the same transformer block again and again on features from each photo, sharpening its estimate of the scene and camera positions with every pass.
Users can choose how many passes to run from one trained model, trading speed for accuracy depending on how much computing power they have. Nvidia says the looped design even beat an otherwise identical version with separate weights for each step, trained on the same data with the same compute.
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How It Stacks Up
On five benchmarks covering indoor rooms, outdoor scenes, single objects and driving footage, Déjà View led on average camera pose accuracy while being the smallest model tested. Its rivals included Depth Anything 3 at 1.2 billion parameters, VGGT at 1.26 billion and π³ at 959 million.
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Originally published on Fathom News.
