DA3 BASE EU AI Act Compliance Profile

Depth Anything 3: DA3-BASE

# noqa: E501

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 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. On this benchmark, DA3 sets a new state-of-the-art across all tasks, surpassing prior SOTA VGGT by an average of 44.3% in camera pose accuracy and 25.1% in geometric accuracy. Moreover, it outperforms DA2 in monocular depth estimation. All models are trained exclusively on public academic datasets.

Model Description

DA3 Base model for multi-view depth estimation and camera pose estimation. Compact foundation model with unified depth-ray representation.

| Property | Value | |----------|-------| | Model Series | Any-view Model | | Parameters | 0.12B | | License | Apache 2.0 |

Capabilities

• ✅ Relative Depth
• ✅ Pose Estimation
• ✅ Pose Conditioning

Quick Start

Installation

git clone https://github.com/ByteDance-Seed/depth-anything-3
cd depth-anythi

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