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--- |
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license: cc-by-nd-4.0 |
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task_categories: |
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- depth-estimation |
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- video-classification |
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- object-detection |
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tags: |
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- camera-calibration |
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- depth-from-defocus |
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- cinema |
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- arri |
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- machine-learning |
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pretty_name: CINE-VBMLR Dataset |
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size_categories: |
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- 10B<n<100B |
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--- |
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# CINE-VBMLR: Cinema-grade Variable Blur Dataset for Camera Tracking |
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## Dataset Summary |
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**CINE-VBMLR** is a specialized dataset designed for high-end cinema camera calibration and tracking. It introduces a novel approach called **CINE-VBMLR** (Cinema Variational Bayesian Multinomial Logistic Regression), adapted from variable blur models originally used in eye-gaze estimation, to solve camera pose and intrinsic parameters in cinematic environments. |
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This dataset was acquired at **MELS Studios** using professional cinema equipment (ARRI), providing high-dynamic-range (HDR) imagery coupled with precise frame-by-frame Lens Data System (LDS) metadata. |
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The primary goal is to train and validate Python-based machine learning models that leverage **Depth from Defocus (DfD)** to improve camera tracking accuracy where traditional pinhole models fail (e.g., shallow depth of field shots). |
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## Dataset Structure |
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### Data Organization |
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The dataset is structured to separate raw linear pixel data from optical metadata: |
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* **`/data`**: Sequences of OpenEXR (`.exr`) files. |
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* **Format:** RGB Float16 (Half) or Float32. |
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* **Color Space:** Linear (converted from ARRI LogC3 via ACES/CST). |
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* **Resolution:** 3200 x 1800 (Source Resolution). |
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* **`/metadata`**: CSV files containing frame-accurate ARRI LDS data. |
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### Metadata Schema (LDS) |
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Each video frame corresponds to a row in the CSV files, containing ground truth values extracted via ARRI Meta Extract: |
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| Column Key | Description | Unit | |
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| :--- | :--- | :--- | |
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| `Master TC` | Source Timecode (aligned with EXR filenames) | HH:MM:SS:FF | |
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| `LDS Focus Distance` | Exact focus distance of the lens | Meters/Feet | |
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| `LDS Iris` | Aperture value (T-Stop) | T-Stop | |
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| `LDS Focal Length` | Focal length (constant or zooming) | mm | |
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| `Camera Tilt` | IMU Tilt data from the camera body | Degrees | |
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| `Camera Roll` | IMU Roll data from the camera body | Degrees | |
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| `Lens Model` | e.g., ARRI Signature Prime 21mm | String | |
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## Acquisition Details |
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* **Location:** MELS Studios (Montreal). |
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* **Camera System:** ARRI ALEXA Mini LF. |
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* **Lens:** ARRI Signature Primes (e.g., 21mm). |
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* **Recording Format:** Apple ProRes 4444 (Converted to Linear EXR for scientific analysis). |
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* **Resolution:** 3.2K (3200x1800). |
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* **Framerate:** 23.976 fps. |
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## Theoretical Background: The CINE-VBMLR Method |
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This dataset supports the **CINE-VBMLR** method, which adapts VBMLR estimation techniques to camera tracking. |
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1. **Origin (VBMLR):** The method derives from the *Variable Blur Model* described in gaze estimation research, where blur circles on the retina (or sensor) are used to infer depth and orientation [1]. |
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2. **Adaptation to Cinema:** Unlike simple eyes or webcams, cinema lenses have complex optical characteristics. We utilize **Depth from Defocus** optimization techniques in the spatial domain [2][3] to model the Point Spread Function (PSF) and Circle of Confusion (CoC). |
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3. **Joint Estimation:** The goal is to perform joint estimation of camera blur and pose [4], using the precise metadata in this dataset as ground truth for supervised learning or validation. |
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## References |
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If you use this dataset, please cite the following foundational papers: |
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* **[1] CINE-VBMLR Foundation (In Preparation):** |
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* Hurtubise, D., et al. *Real-time eye gaze estimation on a computer screen*. (Manuscript in preparation). |
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* **[2] Spatial Domain Defocus:** |
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* Ziou, D., & Deschenes, F. (2001). Depth from defocus estimation in spatial domain. *Computer Vision and Image Understanding*, 81(2), 143-165. |
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* **[3] Optimal Parameters:** |
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* Mannan, F., & Langer, M. S. (2015, October). Optimal camera parameters for depth from defocus. In *2015 International Conference on 3D Vision* (pp. 326-334). IEEE. |
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* **[4] Joint Estimation:** |
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* LeBlanc, J. W., Thelen, B. J., & Hero, A. O. (2018). Joint camera blur and pose estimation from aliased data. *Journal of the Optical Society of America A*, 35(4), 639-651. |
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## License |
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This dataset is licensed under **CC-BY-ND-4.0** (Creative Commons Attribution-NonCommercial 4.0 International). |
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* **Attribution:** You must give appropriate credit to the authors and Studios B79/MELS. |
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* **Non-Commercial:** You may not use this material for commercial purposes without explicit permission. |
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* **CIN-VBMLR Code:** The source code in this repository (hosted on Github) is released under the **Apache License 2.0**. |
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* **CIN-VBMLR Dataset:** The associated dataset and models (hosted on Hugging Face) are released under the **CC-BY-NC-4.0** license. |
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For business cooperation or commercial licensing inquiries, please send an email to **David Hurtubise** at [hurtubisedavid@gmail.com](mailto:hurtubisedavid@gmail.com). |
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**Created by:** David Hurtubise, Djemel Ziou, Marie-Flavie Auclair Fortier (Université de Sherbrooke) |
