Sansjudge/eagle360_test

EAGLE-360 Test Set

Project page: EAGLE-360

Paper: arXiv:2607.02479

EAGLE-360 is a benchmark for embodied active global-to-local exploration in 360-degree panoramic scenes. Given a panoramic image and a target-object query, the model is asked to predict the object's angular position as azimuth and elevation in degrees.

This release contains the public test split only. It includes panoramic images and a annotation file with ground-truth metadata.

Files

eagle360_test/
├── README.md
├── test.json
└── images/

• test.json: 360 test samples.
• images/: 354 unique panoramic images referenced by test.json.

All image paths in test.json are relative to the dataset root.

Data Format

Each sample has the following fields:

{
  "id": "test_000001",
  "panoramic_image": "images/pano_000001_9124.jpg",
  "image_id": "pano_000001",
  "question": "This is a 360-degree panoramic image...",
  "metadata": {
    "object_label": "a flat-screen television",
    "azimuth_degrees": -80.34,
    "elevation_degrees": 0.40,
    "bbox_width_degrees": 7.52,
    "bbox_height_degrees": 10.06,
    "bbox_diagonal_degrees": 12.56,
    "projection_bbox": {
      "x1": 471,
      "y1": 461,
      "x2": 548,
      "y2": 564
    }
  }
}

Citation

If you use this dataset, please cite our paper:

@misc{xu2026eagle360embodiedactiveglobaltolocal,
      title={EAGLE-360: Embodied Active Global-to-Local Exploration in 360$^\circ$},
      author={Jingtao Xu and Zizhuo Lin and Jianwen Sun and Yi Yang and Yawei Luo},
      year={2026},
      eprint={2607.02479},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2607.02479},
}