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CIN-VBMLR

Tasks:
Depth Estimation
Video Classification
Object Detection
Size:
10B<n<100B
Tags:
camera-calibration
depth-from-defocus
cinema
arri
machine-learning
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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-ND-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.

  • CIN-VBMLR Code: The source code in this repository (hosted on Github) is released under the Apache License 2.0.

  • CIN-VBMLR Dataset: The associated dataset and models (hosted on Hugging Face) are released under the CC-BY-NC-4.0 license.

For business cooperation or commercial licensing inquiries, please send an email to David Hurtubise at hurtubisedavid@gmail.com.

Created by: David Hurtubise, Djemel Ziou, Marie-Flavie Auclair Fortier (Université de Sherbrooke)

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