| ---
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| license: cc-by-nc-sa-4.0
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| language:
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| - en
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| pipeline_tag: depth-estimation
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| tags:
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| - depth-estimation
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| - metric-depth-estimation
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| - monocular-depth-estimation
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| - aerial
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| - UAV
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| - drone
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| ---
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|
|
| # OccuFly's Aerial DepthAnythingV2
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|
|
| ## Introduction
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| Following its acceptance as a [CVPR 2026 Oral](https://cvpr.thecvf.com/virtual/2026/oral/40308), we release our fine-tuned [DepthAnythingV2](https://depth-anything-v2.github.io/) model, specialized for aerial imagery. It was trained using the [OccuFly dataset](https://markus-42.github.io/publications/2026/occufly/), the first large-scale, real-world benchmark for aerial Metric Monocular Depth Estimation and Semantic Scene Completion.
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| This model represents the depth estimation component of our [OccuFly project](https://markus-42.github.io/publications/2026/occufly/), in which fine-tuned `DepthAnythingV2-ViT-S` to infer accurate metric depth (in meters) from a single aerial image.
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|
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| ### Key Features
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| - **Aerial-specialized**: Fine-tuned on diverse aerial imagery from urban, industrial, and rural environments.
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| - **Multi-altitude performance**: Trained on data from 50m, 40m, and 30m altitudes.
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| - **Seasonal robustness**: Captures data across all seasons for improved generalization.
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| - **Lightweight**: Uses the ViT-S backbone for efficient inference.
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|
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| ## Installation
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|
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| ```bash
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| git clone https://huggingface.co/spaces/depth-anything/Depth-Anything-V2
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| cd Depth-Anything-V2
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| pip install -r requirements.txt
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| ```
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|
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| ## Quickstart
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| Download the [model checkpoint](https://huggingface.co/markus-42/OccuFly-DepthAnythingV2/resolve/main/OccuFly-DepthAnything2.pth) and place it in your desired directory:
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|
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| ```python
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| import cv2
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| import torch
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| from depth_anything_v2.dpt import DepthAnythingV2
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|
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| # Load the fine-tuned aerial model
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| model = DepthAnythingV2(encoder='vits', features=64, out_channels=[48, 96, 192, 384])
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| model.load_state_dict(torch.load('OccuFly-DepthAnything2.pth', map_location='cpu'))
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| model.eval()
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|
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| # Inference
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| with torch.no_grad():
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| raw_img = cv2.imread('example.jpg')
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| depth = model.infer_image(raw_img) # HxW metric depth map
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| ```
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|
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| ## OccuFly Dataset
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| The model is fine-tuned on [OccuFly](https://huggingface.co/datasets/markus-42/OccuFly), which includes:
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| - **20,000+ aerial RGB images** with corresponding depth maps
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| - **Multiple altitudes**: 30m, 40m, 50m flight altitudes
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| - **Seasonal diversity**: Spring, Summer, Fall, Winter
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| - **Multiple environments**: Urban, industrial, rural
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| - **21 semantic classes** with dense voxel grid annotations
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|
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| ## Citation
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| If our work was helpful to you, we would appreciate citing our paper and the original DepthAnythingV2 work, or giving the repository a like ❤️
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|
|
| ```bibtex
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| @inproceedings{gross2026occufly,
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| title={{OccuFly: A 3D Vision Benchmark for Semantic Scene Completion from the Aerial Perspective}},
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| author={Markus Gross and Sai B. Matha and Aya Fahmy and Rui Song and Daniel Cremers and Henri Meess},
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| booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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| year={2026},
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| }
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|
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| @article{depth_anything_v2,
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| title={Depth Anything V2},
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| author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
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| journal={arXiv preprint arXiv:2406.09414},
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| year={2024}
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| }
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| ```
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|
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| ## Related Resources
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|
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| 🌐 [OccuFly Project Page](https://markus-42.github.io/publications/2026/occufly/)<br>
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| 🤗 [OccuFly Dataset on HuggingFace](https://huggingface.co/datasets/markus-42/OccuFly)<br>
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| 📜 [OccuFly Paper](https://arxiv.org/abs/2512.20770)<br>
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| 🌐 [Original DepthAnythingV2](https://github.com/DepthAnything/Depth-Anything-V2)
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|
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| ## License
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|
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| This work is licensed under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/). See the LICENSE file for the full legal terms.
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|
