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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- depth-estimation
+- computer-vision
+- monocular-depth
+- multi-view-geometry
+- pose-estimation
+library_name: depth-anything-3
+pipeline_tag: depth-estimation
+---
+
+# Depth Anything 3: DA3MONO-LARGE
+
+<div align="center">
+
+[![Project Page](https://img.shields.io/badge/Project_Page-Depth_Anything_3-green)](https://depth-anything-3.github.io)
+[![Paper](https://img.shields.io/badge/arXiv-Depth_Anything_3-red)](https://arxiv.org/abs/)
+[![Demo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue)](https://huggingface.co/spaces/depth-anything/Depth-Anything-3)  # noqa: E501
+<!-- Benchmark badge removed as per request -->
+
+</div>
+
+## Model Description
+
+DA3 Monocular Large model for high-quality relative monocular depth estimation. Unlike disparity-based models (e.g., Depth Anything 2), it directly predicts depth, resulting in superior geometric accuracy.
+
+| Property | Value |
+|----------|-------|
+| **Model Series** | Monocular Depth |
+| **Parameters** | 0.35B |
+| **License** | Apache 2.0 |
+
+
+
+## Capabilities
+
+- ✅ Relative Depth
+- ✅ Sky Segmentation
+
+## Quick Start
+
+### Installation
+
+```bash
+git clone https://github.com/ByteDance-Seed/depth-anything-3
+cd depth-anything-3
+pip install -e .
+```
+
+### Basic Example
+
+```python
+import torch
+from depth_anything_3.api import DepthAnything3
+
+# Load model from Hugging Face Hub
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = DepthAnything3.from_pretrained("depth-anything/da3mono-large")
+model = model.to(device=device)
+
+# Run inference on images
+images = ["image1.jpg", "image2.jpg"]  # List of image paths, PIL Images, or numpy arrays
+prediction = model.inference(
+    images,
+    export_dir="output",
+    export_format="glb"  # Options: glb, npz, ply, mini_npz, gs_ply, gs_video
+)
+
+# Access results
+print(prediction.depth.shape)        # Depth maps: [N, H, W] float32
+print(prediction.conf.shape)         # Confidence maps: [N, H, W] float32
+print(prediction.extrinsics.shape)   # Camera poses (w2c): [N, 3, 4] float32
+print(prediction.intrinsics.shape)   # Camera intrinsics: [N, 3, 3] float32
+```
+
+### Command Line Interface
+
+```bash
+# Process images with auto mode
+da3 auto path/to/images \
+    --export-format glb \
+    --export-dir output \
+    --model-dir depth-anything/da3mono-large
+
+# Use backend for faster repeated inference
+da3 backend --model-dir depth-anything/da3mono-large
+da3 auto path/to/images --export-format glb --use-backend
+```
+
+## Model Details
+
+- **Developed by:** ByteDance Seed Team
+- **Model Type:** Vision Transformer for Visual Geometry
+- **Architecture:** Plain transformer with unified depth-ray representation
+- **Training Data:** Public academic datasets only
+
+### Key Insights
+
+💎 A **single plain transformer** (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization.  # noqa: E501
+
+✨ A singular **depth-ray representation** obviates the need for complex multi-task learning.
+
+## Performance
+
+🏆 Depth Anything 3 significantly outperforms:
+- **Depth Anything 2** for monocular depth estimation
+- **VGGT** for multi-view depth estimation and pose estimation
+
+For detailed benchmarks, please refer to our [paper](https://depth-anything-3.github.io).  # noqa: E501
+
+## Limitations
+
+- The model is trained on academic datasets and may have limitations on certain domain-specific images  # noqa: E501
+- Performance may vary depending on image quality, lighting conditions, and scene complexity
+
+
+## Citation
+
+If you find Depth Anything 3 useful in your research or projects, please cite:
+
+```bibtex
+@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},  # noqa: E501
+  journal={arXiv preprint arXiv:XXXX.XXXXX},
+  year={2025}
+}
+```
+
+## Links
+
+- 🏠 [Project Page](https://depth-anything-3.github.io)
+- 📄 [Paper](https://arxiv.org/abs/)
+- 💻 [GitHub Repository](https://github.com/ByteDance-Seed/depth-anything-3)
+- 🤗 [Hugging Face Demo](https://huggingface.co/spaces/depth-anything/Depth-Anything-3)
+- 📚 [Documentation](https://github.com/ByteDance-Seed/depth-anything-3#-useful-documentation)
+
+## Authors
+
+[Haotong Lin](https://haotongl.github.io/) · [Sili Chen](https://github.com/SiliChen321) · [Junhao 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://bingykang.github.io/)  # noqa: E501

config.json

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+{
+  "model_name": "da3mono-large",
+  "config": {
+    "__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": "vitl",
+      "out_layers": [
+        4,
+        11,
+        17,
+        23
+      ],
+      "alt_start": -1,
+      "qknorm_start": -1,
+      "rope_start": -1,
+      "cat_token": false
+    },
+    "head": {
+      "__object__": {
+        "path": "depth_anything_3.model.dpt",
+        "name": "DPT",
+        "args": "as_params"
+      },
+      "dim_in": 1024,
+      "output_dim": 1,
+      "features": 256,
+      "out_channels": [
+        256,
+        512,
+        1024,
+        1024
+      ]
+    }
+  }
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7a799a7f95eb8d4c404c2ca8be3dc3276b350a417ddc4420db72ba850cc0e960
+size 1336734448