Depth Anything 3: Recovering the Visual Space from Any Views

[**Haotong Lin**](https://haotongl.github.io/)* · [**Sili Chen**](https://github.com/SiliChen321)* · [**Jun Hao Liew**](https://liewjunhao.github.io/)* · [**Donny Y. Chen**](https://donydchen.github.io)* · [**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)
[**Bingyi Kang**](https://bingyikang.com/)*† †project lead *Equal Contribution Paper PDF Project Page
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**.

Depth Anything 3 - Left

Depth Anything 3

## 📰 News - **11-12-2025:** 🚀 New models and [**DA3-Streaming**](da3_streaming/README.md) released! Handle ultra-long video sequence inference with less than 12GB GPU memory via sliding-window streaming inference. Special thanks to [Kai Deng](https://github.com/DengKaiCQ) for his contribution to DA3-Streaming! - **08-12-2025:** 📊 [Benchmark evaluation pipeline](docs/BENCHMARK.md) released! Evaluate pose estimation & 3D reconstruction on 5 datasets. - **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. ## 🚀 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) - 📊 [Benchmark Evaluation](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. ⚠️ Models with the `-1.1` suffix are retrained after fixing a training bug; prefer these refreshed checkpoints. The original `DA3NESTED-GIANT-LARGE`, `DA3-GIANT`, and `DA3-LARGE` remain available but are deprecated. You could expect much better performance for street scenes with the `-1.1` models. | 🗃️ Model Name | 📏 Params | 📊 Rel. Depth | 📷 Pose Est. | 🧭 Pose Cond. | 🎨 GS | 📐 Met. Depth | ☁️ Sky Seg | 📄 License | |-------------------------------|-----------|---------------|--------------|---------------|-------|---------------|-----------|----------------| | **Nested** | | | | | | | | | | [DA3NESTED-GIANT-LARGE-1.1](https://huggingface.co/depth-anything/DA3NESTED-GIANT-LARGE-1.1) | 1.40B | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | CC BY-NC 4.0 | | [DA3NESTED-GIANT-LARGE](https://huggingface.co/depth-anything/DA3NESTED-GIANT-LARGE) | 1.40B | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | CC BY-NC 4.0 | | **Any-view Model** | | | | | | | | | | [DA3-GIANT-1.1](https://huggingface.co/depth-anything/DA3-GIANT-1.1) | 1.15B | ✅ | ✅ | ✅ | ✅ | | | CC BY-NC 4.0 | | [DA3-GIANT](https://huggingface.co/depth-anything/DA3-GIANT) | 1.15B | ✅ | ✅ | ✅ | ✅ | | | CC BY-NC 4.0 | | [DA3-LARGE-1.1](https://huggingface.co/depth-anything/DA3-LARGE-1.1) | 0.35B | ✅ | ✅ | ✅ | | | | 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. - **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.
AUC3 Results for DA3NESTED-GIANT-LARGE | 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 |
- **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. - [DA3-ROS2-CPP-TensorRT](https://github.com/ika-rwth-aachen/ros2-depth-anything-v3-trt): DA3 ROS2 C++ TensorRT Inference Node: a ROS2 node for DA3 depth estimation using TensorRT for real-time inference. - [VideoDepthViewer3D](https://github.com/amariichi/VideoDepthViewer3D): Streaming videos with DA3 metric depth to a Three.js/WebXR 3D viewer for VR/stereo playback. ## 🧑‍💻 Official Codebase Core Contributors and Maintainers

Bingyi Kang
Haotong Lin
Sili Chen
Jun Hao Liew
Donny Y. Chen
Kai Deng
## 📝 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} } ```