Update README.md

README.md

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----
-license: cc-by-nd-4.0
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+---
+license: cc-by-nd-4.0
+task_categories:
+- depth-estimation
+- video-classification
+- object-detection
+tags:
+- camera-calibration
+- depth-from-defocus
+- cinema
+- arri
+- machine-learning
+pretty_name: CINE-VBMLR Dataset
+size_categories:
+- 10B<n<100B
+---
+
+# CINE-VBMLR: Cinema-grade Variable Blur Dataset for Camera Tracking
+
+## Dataset Summary
+
+**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.
+
+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.
+
+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).
+
+## Dataset Structure
+
+### Data Organization
+The dataset is structured to separate raw linear pixel data from optical metadata:
+
+* **`/data`**: Sequences of OpenEXR (`.exr`) files.
+    * **Format:** RGB Float16 (Half) or Float32.
+    * **Color Space:** Linear (converted from ARRI LogC3 via ACES/CST).
+    * **Resolution:** 3200 x 1800 (Source Resolution).
+* **`/metadata`**: CSV files containing frame-accurate ARRI LDS data.
+
+### Metadata Schema (LDS)
+Each video frame corresponds to a row in the CSV files, containing ground truth values extracted via ARRI Meta Extract:
+
+| Column Key | Description | Unit |
+| :--- | :--- | :--- |
+| `Master TC` | Source Timecode (aligned with EXR filenames) | HH:MM:SS:FF |
+| `LDS Focus Distance` | Exact focus distance of the lens | Meters/Feet |
+| `LDS Iris` | Aperture value (T-Stop) | T-Stop |
+| `LDS Focal Length` | Focal length (constant or zooming) | mm |
+| `Camera Tilt` | IMU Tilt data from the camera body | Degrees |
+| `Camera Roll` | IMU Roll data from the camera body | Degrees |
+| `Lens Model` | e.g., ARRI Signature Prime 21mm | String |
+
+## Acquisition Details
+
+* **Location:** MELS Studios (Montreal).
+* **Camera System:** ARRI ALEXA Mini LF.
+* **Lens:** ARRI Signature Primes (e.g., 21mm).
+* **Recording Format:** Apple ProRes 4444 (Converted to Linear EXR for scientific analysis).
+* **Resolution:** 3.2K (3200x1800).
+* **Framerate:** 23.976 fps.
+
+## Theoretical Background: The CINE-VBMLR Method
+
+This dataset supports the **CINE-VBMLR** method, which adapts VBMLR estimation techniques to camera tracking.
+
+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].
+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).
+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.
+
+## References
+
+If you use this dataset, please cite the following foundational papers:
+
+* **[1] CINE-VBMLR Foundation (In Preparation):**
+  * Hurtubise, D., et al. *Real-time eye gaze estimation on a computer screen*. (Manuscript in preparation).
+
+* **[2] Spatial Domain Defocus:**
+  * Ziou, D., & Deschenes, F. (2001). Depth from defocus estimation in spatial domain. *Computer Vision and Image Understanding*, 81(2), 143-165.
+
+* **[3] Optimal Parameters:**
+  * 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.
+
+* **[4] Joint Estimation:**
+  * 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.
+
+## License
+
+This dataset is licensed under **CC-BY-NC-4.0** (Creative Commons Attribution-NonCommercial 4.0 International).
+
+* **Attribution:** You must give appropriate credit to the authors and Studios B79/MELS.
+* **Non-Commercial:** You may not use this material for commercial purposes without explicit permission.
+
+---
+**Created by:** David Hurtubise, Djemel Ziou, Marie-Flavie Auclair Fortier (Université de Sherbrooke)