DL3DV-Absolute-Camera

Per-frame camera parameter annotations for the DL3DV dataset (6,377 scenes across 7 buckets 1K–7K; 2,161,003 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

The archive mirrors the source DL3DV-ALL-960P layout: one .zip per scene, grouped into bucket folders — <bucket>/<scene_hash>.zip (e.g. 1K/001dccbc…740d5f.zip). The scene hashes match DL3DV-ALL-960P, so the camera annotations pair 1:1 with the source frames.

Each zip unpacks to the original per-frame layout:

dense/camera/frame_00001.json
dense/camera/frame_00002.json
...

Each JSON holds the predicted monocular camera parameters for that frame:

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 (dense/camera/frame_00001.json):

{"roll": 0.0044, "pitch": 0.1566, "vfov": 1.0864, "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).

split	roll μ / med / σ	pitch μ / med / σ	FoV μ / med / σ
all (2.16M frames)	−0.3° / −0.3° / 4.9°	−7.0° / −6.1° / 16.0°	53.4° / 54.8° / 8.7°

Reading the distributions

• Roll is tightly peaked at 0° (σ ≈ 4.9°) — DL3DV captures are shot level.
• Pitch carries a downward bias (median ≈ −6°, mean ≈ −7°) with a wide spread (σ ≈ 16°), reflecting the varied up/down camera motion across scenes.
• FoV concentrates around 50–55° (median ≈ 55°) — moderately wide video capture, ranging ~20–90°.

Dataset Download

You can download the entire dataset using the following command:

hf download KangLiao/DL3DV-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}
  }