Depth Anything 3: Recovering the Visual Space from Any Views

Understand

We present Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses.

• In pursuit of minimal modeling, DA3 yields two key insights: a single plain transformer (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization, and a singular depth-ray prediction target obviates the need for complex multi-task learning.
• Through our teacher-student training paradigm, the model achieves a level of detail and generalization on par with Depth Anything 2 (DA2).
• We establish a new visual geometry benchmark covering camera pose estimation, any-view geometry and visual rendering.