Datasets:

markus-42
/

OccuFly

dataset_info:
  features:
    - name: scene
      dtype: int32
    - name: altitude
      dtype: int32
    - name: frame_id
      dtype: int32
    - name: image
      dtype: image
    - name: depth_map
      dtype: array3d
    - name: voxel_grid
      dtype:
        label:
          dtype: uint8
          shape:
            - 192
            - 128
            - 128
        invalid:
          dtype: bool
          shape:
            - 192
            - 128
            - 128
        occluded:
          dtype: bool
          shape:
            - 192
            - 128
            - 128
        surface:
          dtype: bool
          shape:
            - 192
            - 128
            - 128
    - name: calibration
      dtype:
        K:
          dtype: float32
          shape:
            - 3
            - 3
language:
  - en
license: cc-by-nc-sa-4.0
tags:
  - 3d scene understanding
  - 3d-scene-completion
  - aerial perception
  - autonomous flying
  - dataset
  - benchmark
task_categories:
  - image-to-3d
task_ids:
  - semantic-segmentation

OccuFly Dataset

Following its acceptance as a CVPR 2026 Oral, we release OccuFly: the first real-world, large-scale camera-based benchmark for Semantic Scene Completion and Metric Monocular Depth Estimation from the aerial perspective.

📚 Full Documentation on GitHub: github.com/markus-42/occufly
🌐 Project Page: markus-42.github.io/publications/2026/occufly/
🤗 Aerial DepthAnything2: huggingface.co/markus-42/OccuFly-DepthAnythingV2
📜 Paper: arXiv:2512.20770

Download and Documentation

For details on download and documentation, visit github.com/markus-42/occufly

Citation

If this repository or our work was helpful to you, we would appreciate citing our paper and giving the repository a like ❤️

@inproceedings{gross2026occufly,
    title={{OccuFly: A 3D Vision Benchmark for Semantic Scene Completion from the Aerial Perspective}}, 
    author={Markus Gross and Sai B. Matha and Aya Fahmy and Rui Song and Daniel Cremers and Henri Meess},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2026},
}

License

This work is licensed under the CC BY-NC-SA 4.0 license. See the LICENSE file for the full legal terms.