Title: Depth Anything 3: Recovering the Visual Space from Any Views
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Depth Estimation

Abstract: 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 DINOv2 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. On this benchmark, DA3 sets a new state-of-the-art across all tasks, surpassing prior SOTA VGGT by an average of 35.7\% in camera pose accuracy and 23.6\% in geometric accuracy. Moreover, it outperforms DA2 in monocular depth estimation. All models are trained exclusively on public academic datasets.

TL;DR: Depth Anything 3 uses a single vanilla DINOv2 transformer to take arbitrary input views and outputs consistent depth and ray maps, delivering leading pose, geometry, and visual rendering performance.