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roll
float64
pitch
float64
vfov
float64
k1
float64
parse_ok
bool
-0.0064
-0.0664
0.9666
0
true
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0
true
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0
true
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true
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End of preview.

RealEstate10K-Absolute-Camera

camera map collage

Per-frame camera parameter annotations for the RealEstate10K (RE10K) dataset (5,134 scenes; 595,382 valid per-frame annotations), captioned by the Puffin-World model.

The collage above visualizes the camera maps on sample images — each pair shows the up field (green arrows: the projected gravity-up direction) and the latitude field (colored contours: angle above/below the horizon).

Format

One .zip per scene<scene_id>.zip (e.g. 0000cc6d8b108390.zip), scene ids matching the RealEstate10K clips.

Each zip unpacks to the original per-frame layout:

camera/52553000.json
camera/52603000.json
...

Each JSON holds the predicted monocular camera parameters:

field meaning unit
roll camera roll radians
pitch camera pitch radians
vfov vertical field of view radians
k1 radial distortion coefficient
parse_ok whether the model output parsed within valid ranges bool

Example:

{"roll": -0.0040, "pitch": -0.0667, "vfov": 0.9645, "k1": 0.0, "parse_ok": true}

Camera Parameter Distributions

Histograms of the predicted roll / pitch / vertical-FoV over all frames (proportion of valid samples per 10° bin; parse_ok=False samples excluded).

re10k camera stats

split roll μ / med / σ pitch μ / med / σ FoV μ / med / σ
all (595K frames) -0.2° / -0.2° / 1.8° -5.2° / -3.8° / 7.9° 51.4° / 55.3° / 10.2°

Reading the distributions

  • Roll is tightly peaked at 0° (σ ≈ 1.8°) — captures are shot close to level.
  • Pitch carries a slight downward bias (median ≈ -3.8°, mean ≈ -5.2°) with σ ≈ 10–8° spread.
  • FoV concentrates around 45–65° (median ≈ 55.3°).

Dataset Download

You can download the entire dataset using the following command:

hf download KangLiao/RealEstate10K-Absolute-Camera --repo-type dataset

Caption Pipeline

Beyond this captioned dataset, we also release a complete captioning pipeline for annotating camera parameters for arbitrary datasets, analyzing camera parameter distributions, and visualizing the corresponding camera maps. The pipeline is available in our GitHub repository.

Citation

If you find the captioned dataset useful for your research or applications, please cite our paper using the following BibTeX:

  @article{liao2025puffin,
    title={Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generation},
    author={Liao, Kang and Wu, Size and Wu, Zhonghua and Jin, Linyi and Wang, Chao and Wang, Yikai and Wang, Fei and Li, Wei and Loy, Chen Change},
    journal={arXiv preprint arXiv:2510.08673},
    year={2025}
  }
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