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<div align="center">
<h1 style="border-bottom: none; margin-bottom: 0px ">Depth Anything 3: Recovering the Visual Space from Any Views</h1>
<!-- <h2 style="border-top: none; margin-top: 3px;">Recovering the Visual Space from Any Views</h2> -->
[**Haotong Lin**](https://haotongl.github.io/)<sup>&ast;</sup> · [**Sili Chen**](https://github.com/SiliChen321)<sup>&ast;</sup> · [**Jun Hao Liew**](https://liewjunhao.github.io/)<sup>&ast;</sup> · [**Donny Y. Chen**](https://donydchen.github.io)<sup>&ast;</sup> · [**Zhenyu Li**](https://zhyever.github.io/) · [**Guang Shi**](https://scholar.google.com/citations?user=MjXxWbUAAAAJ&hl=en) · [**Jiashi Feng**](https://scholar.google.com.sg/citations?user=Q8iay0gAAAAJ&hl=en)
<br>
[**Bingyi Kang**](https://bingykang.github.io/)<sup>&ast;&dagger;</sup>
&dagger;project lead&emsp;&ast;Equal Contribution
<a href="https://arxiv.org/abs/2511.10647"><img src='https://img.shields.io/badge/arXiv-Depth Anything 3-red' alt='Paper PDF'></a>
<a href='https://depth-anything-3.github.io'><img src='https://img.shields.io/badge/Project_Page-Depth Anything 3-green' alt='Project Page'></a>
<a href='https://huggingface.co/spaces/depth-anything/Depth-Anything-3'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue'></a>
<!-- <a href='https://huggingface.co/datasets/depth-anything/VGB'><img src='https://img.shields.io/badge/Benchmark-VisGeo-yellow' alt='Benchmark'></a> -->
<!-- <a href='https://huggingface.co/datasets/depth-anything/data'><img src='https://img.shields.io/badge/Benchmark-xxx-yellow' alt='Data'></a> -->
</div>
This work presents **Depth Anything 3 (DA3)**, a model that predicts spatially consistent geometry from
arbitrary 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,
- ✨ A singular **depth-ray representation** obviates the need for complex multi-task learning.
🏆 DA3 significantly outperforms
[DA2](https://github.com/DepthAnything/Depth-Anything-V2) for monocular depth estimation,
and [VGGT](https://github.com/facebookresearch/vggt) for multi-view depth estimation and pose estimation.
All models are trained exclusively on **public academic datasets**.
<!-- <p align="center">
<img src="assets/images/da3_teaser.png" alt="Depth Anything 3" width="100%">
</p> -->
<p align="center">
<img src="assets/images/demo320-2.gif" alt="Depth Anything 3 - Left" width="70%">
</p>
<p align="center">
<img src="assets/images/da3_radar.png" alt="Depth Anything 3" width="100%">
</p>
## 📰 News
- **30-11-2025:** Add [`use_ray_pose`](#use-ray-pose) and [`ref_view_strategy`](docs/funcs/ref_view_strategy.md) (reference view selection for multi-view inputs).
- **25-11-2025:** Add [Awesome DA3 Projects](#-awesome-da3-projects), a community-driven section featuring DA3-based applications.
- **14-11-2025:** Paper, project page, code and models are all released.
## ✨ Highlights
### 🏆 Model Zoo
We release three series of models, each tailored for specific use cases in visual geometry.
- 🌟 **DA3 Main Series** (`DA3-Giant`, `DA3-Large`, `DA3-Base`, `DA3-Small`) These are our flagship foundation models, trained with a unified depth-ray representation. By varying the input configuration, a single model can perform a wide range of tasks:
+ 🌊 **Monocular Depth Estimation**: Predicts a depth map from a single RGB image.
+ 🌊 **Multi-View Depth Estimation**: Generates consistent depth maps from multiple images for high-quality fusion.
+ 🎯 **Pose-Conditioned Depth Estimation**: Achieves superior depth consistency when camera poses are provided as input.
+ 📷 **Camera Pose Estimation**: Estimates camera extrinsics and intrinsics from one or more images.
+ 🟡 **3D Gaussian Estimation**: Directly predicts 3D Gaussians, enabling high-fidelity novel view synthesis.
- 📐 **DA3 Metric Series** (`DA3Metric-Large`) A specialized model fine-tuned for metric depth estimation in monocular settings, ideal for applications requiring real-world scale.
- 🔍 **DA3 Monocular Series** (`DA3Mono-Large`). A dedicated model for high-quality relative monocular depth estimation. Unlike disparity-based models (e.g., [Depth Anything 2](https://github.com/DepthAnything/Depth-Anything-V2)), it directly predicts depth, resulting in superior geometric accuracy.
🔗 Leveraging these available models, we developed a **nested series** (`DA3Nested-Giant-Large`). This series combines a any-view giant model with a metric model to reconstruct visual geometry at a real-world metric scale.
### 🛠️ Codebase Features
Our repository is designed to be a powerful and user-friendly toolkit for both practical application and future research.
- 🎨 **Interactive Web UI & Gallery**: Visualize model outputs and compare results with an easy-to-use Gradio-based web interface.
- ⚡ **Flexible Command-Line Interface (CLI)**: Powerful and scriptable CLI for batch processing and integration into custom workflows.
- 💾 **Multiple Export Formats**: Save your results in various formats, including `glb`, `npz`, depth images, `ply`, 3DGS videos, etc, to seamlessly connect with other tools.
- 🔧 **Extensible and Modular Design**: The codebase is structured to facilitate future research and the integration of new models or functionalities.
<!-- ### 🎯 Visual Geometry Benchmark
We introduce a new benchmark to rigorously evaluate geometry prediction models on three key tasks: pose estimation, 3D reconstruction, and visual rendering (novel view synthesis) quality.
- 🔄 **Broad Model Compatibility**: Our benchmark is designed to be versatile, supporting the evaluation of various models, including both monocular and multi-view depth estimation approaches.
- 🔬 **Robust Evaluation Pipeline**: We provide a standardized pipeline featuring RANSAC-based pose alignment, TSDF fusion for dense reconstruction, and a principled view selection strategy for novel view synthesis.
- 📊 **Standardized Metrics**: Performance is measured using established metrics: AUC for pose accuracy, F1-score and Chamfer Distance for reconstruction, and PSNR/SSIM/LPIPS for rendering quality.
- 🌍 **Diverse and Challenging Datasets**: The benchmark spans a wide range of scenes from datasets like HiRoom, ETH3D, DTU, 7Scenes, ScanNet++, DL3DV, Tanks and Temples, and MegaDepth. -->
## 🚀 Quick Start
### 📦 Installation
```bash
pip install xformers torch\>=2 torchvision
pip install -e . # Basic
pip install --no-build-isolation git+https://github.com/nerfstudio-project/gsplat.git@0b4dddf04cb687367602c01196913cde6a743d70 # for gaussian head
pip install -e ".[app]" # Gradio, python>=3.10
pip install -e ".[all]" # ALL
```
For detailed model information, please refer to the [Model Cards](#-model-cards) section below.
### 💻 Basic Usage
```python
import glob, os, torch
from depth_anything_3.api import DepthAnything3
device = torch.device("cuda")
model = DepthAnything3.from_pretrained("depth-anything/DA3NESTED-GIANT-LARGE")
model = model.to(device=device)
example_path = "assets/examples/SOH"
images = sorted(glob.glob(os.path.join(example_path, "*.png")))
prediction = model.inference(
images,
)
# prediction.processed_images : [N, H, W, 3] uint8 array
print(prediction.processed_images.shape)
# prediction.depth : [N, H, W] float32 array
print(prediction.depth.shape)
# prediction.conf : [N, H, W] float32 array
print(prediction.conf.shape)
# prediction.extrinsics : [N, 3, 4] float32 array # opencv w2c or colmap format
print(prediction.extrinsics.shape)
# prediction.intrinsics : [N, 3, 3] float32 array
print(prediction.intrinsics.shape)
```
```bash
export MODEL_DIR=depth-anything/DA3NESTED-GIANT-LARGE
# This can be a Hugging Face repository or a local directory
# If you encounter network issues, consider using the following mirror: export HF_ENDPOINT=https://hf-mirror.com
# Alternatively, you can download the model directly from Hugging Face
export GALLERY_DIR=workspace/gallery
mkdir -p $GALLERY_DIR
# CLI auto mode with backend reuse
da3 backend --model-dir ${MODEL_DIR} --gallery-dir ${GALLERY_DIR} # Cache model to gpu
da3 auto assets/examples/SOH \
--export-format glb \
--export-dir ${GALLERY_DIR}/TEST_BACKEND/SOH \
--use-backend
# CLI video processing with feature visualization
da3 video assets/examples/robot_unitree.mp4 \
--fps 15 \
--use-backend \
--export-dir ${GALLERY_DIR}/TEST_BACKEND/robo \
--export-format glb-feat_vis \
--feat-vis-fps 15 \
--process-res-method lower_bound_resize \
--export-feat "11,21,31"
# CLI auto mode without backend reuse
da3 auto assets/examples/SOH \
--export-format glb \
--export-dir ${GALLERY_DIR}/TEST_CLI/SOH \
--model-dir ${MODEL_DIR}
```
The model architecture is defined in [`DepthAnything3Net`](src/depth_anything_3/model/da3.py), and specified with a Yaml config file located at [`src/depth_anything_3/configs`](src/depth_anything_3/configs). The input and output processing are handled by [`DepthAnything3`](src/depth_anything_3/api.py). To customize the model architecture, simply create a new config file (*e.g.*, `path/to/new/config`) as:
```yaml
__object__:
path: depth_anything_3.model.da3
name: DepthAnything3Net
args: as_params
net:
__object__:
path: depth_anything_3.model.dinov2.dinov2
name: DinoV2
args: as_params
name: vitb
out_layers: [5, 7, 9, 11]
alt_start: 4
qknorm_start: 4
rope_start: 4
cat_token: True
head:
__object__:
path: depth_anything_3.model.dualdpt
name: DualDPT
args: as_params
dim_in: &head_dim_in 1536
output_dim: 2
features: &head_features 128
out_channels: &head_out_channels [96, 192, 384, 768]
```
Then, the model can be created with the following code snippet.
```python
from depth_anything_3.cfg import create_object, load_config
Model = create_object(load_config("path/to/new/config"))
```
## 📚 Useful Documentation
- 🖥️ [Command Line Interface](docs/CLI.md)
- 📑 [Python API](docs/API.md)
<!-- - 🏁 [Visual Geometry Benchmark](docs/BENCHMARK.md) -->
## 🗂️ Model Cards
Generally, you should observe that DA3-LARGE achieves comparable results to VGGT.
The Nested series uses an Any-view model to estimate pose and depth, and a monocular metric depth estimator for scaling.
| 🗃️ Model Name | 📏 Params | 📊 Rel. Depth | 📷 Pose Est. | 🧭 Pose Cond. | 🎨 GS | 📐 Met. Depth | ☁️ Sky Seg | 📄 License |
|-------------------------------|-----------|---------------|--------------|---------------|-------|---------------|-----------|----------------|
| **Nested** | | | | | | | | |
| [DA3NESTED-GIANT-LARGE](https://huggingface.co/depth-anything/DA3NESTED-GIANT-LARGE) | 1.40B | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | CC BY-NC 4.0 |
| **Any-view Model** | | | | | | | | |
| [DA3-GIANT](https://huggingface.co/depth-anything/DA3-GIANT) | 1.15B | ✅ | ✅ | ✅ | ✅ | | | CC BY-NC 4.0 |
| [DA3-LARGE](https://huggingface.co/depth-anything/DA3-LARGE) | 0.35B | ✅ | ✅ | ✅ | | | | CC BY-NC 4.0 |
| [DA3-BASE](https://huggingface.co/depth-anything/DA3-BASE) | 0.12B | ✅ | ✅ | ✅ | | | | Apache 2.0 |
| [DA3-SMALL](https://huggingface.co/depth-anything/DA3-SMALL) | 0.08B | ✅ | ✅ | ✅ | | | | Apache 2.0 |
| | | | | | | | | |
| **Monocular Metric Depth** | | | | | | | | |
| [DA3METRIC-LARGE](https://huggingface.co/depth-anything/DA3METRIC-LARGE) | 0.35B | ✅ | | | | ✅ | ✅ | Apache 2.0 |
| | | | | | | | | |
| **Monocular Depth** | | | | | | | | |
| [DA3MONO-LARGE](https://huggingface.co/depth-anything/DA3MONO-LARGE) | 0.35B | ✅ | | | | | ✅ | Apache 2.0 |
## ❓ FAQ
- **Monocular Metric Depth**: To obtain metric depth in meters from `DA3METRIC-LARGE`, use `metric_depth = focal * net_output / 300.`, where `focal` is the focal length in pixels (typically the average of fx and fy from the camera intrinsic matrix K). Note that the output from `DA3NESTED-GIANT-LARGE` is already in meters.
- <a id="use-ray-pose"></a>**Ray Head (`use_ray_pose`)**: Our API and CLI support `use_ray_pose` arg, which means that the model will derive camera pose from ray head, which is generally slightly slower, but more accurate. Note that the default is `False` for faster inference speed.
<details>
<summary>AUC3 Results for DA3NESTED-GIANT-LARGE</summary>
| Model | HiRoom | ETH3D | DTU | 7Scenes | ScanNet++ |
|-------|------|-------|-----|---------|-----------|
| `ray_head` | 84.4 | 52.6 | 93.9 | 29.5 | 89.4 |
| `cam_head` | 80.3 | 48.4 | 94.1 | 28.5 | 85.0 |
</details>
- **Older GPUs without XFormers support**: See [Issue #11](https://github.com/ByteDance-Seed/Depth-Anything-3/issues/11). Thanks to [@S-Mahoney](https://github.com/S-Mahoney) for the solution!
## 🏢 Awesome DA3 Projects
A community-curated list of Depth Anything 3 integrations across 3D tools, creative pipelines, robotics, and web/VR viewers, including but not limited to these. You are welcome to submit your DA3-based project via PR, and we will review and feature it if applicable.
- [DA3-blender](https://github.com/xy-gao/DA3-blender): Blender addon for DA3-based 3D reconstruction from a set of images.
- [ComfyUI-DepthAnythingV3](https://github.com/PozzettiAndrea/ComfyUI-DepthAnythingV3): ComfyUI nodes for Depth Anything 3, supporting single/multi-view and video-consistent depth with optional point‑cloud export.
- [DA3-ROS2-Wrapper](https://github.com/GerdsenAI/GerdsenAI-Depth-Anything-3-ROS2-Wrapper): Real-time DA3 depth in ROS2 with multi-camera support.
- [VideoDepthViewer3D](https://github.com/amariichi/VideoDepthViewer3D): Streaming videos with DA3 metric depth to a Three.js/WebXR 3D viewer for VR/stereo playback.
## 📝 Citations
If you find Depth Anything 3 useful in your research or projects, please cite our work:
```
@article{depthanything3,
title={Depth Anything 3: Recovering the visual space from any views},
author={Haotong Lin and Sili Chen and Jun Hao Liew and Donny Y. Chen and Zhenyu Li and Guang Shi and Jiashi Feng and Bingyi Kang},
journal={arXiv preprint arXiv:2511.10647},
year={2025}
}
```