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[I3D 2024] Deblur-GS: 3D Gaussian Splatting from Camera Motion Blurred Images

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Gaussian Splats for Real World Video

This adapts gaussian splats to be used on amateur(sparse, shaky, blurry) iphone data.

This adds:

This started as a fork from:

Deblur-GS: 3D Gaussian Splatting from Camera Motion Blurred Images Official implementation of paper "Deblur-GS: 3D Gaussian Splatting from Camera Motion Blurred Images", I3D 2024

Installation

SET DISTUTILS_USE_SDK=1 # Windows only
conda env create --file environment.yml
conda activate deblur_gs

Running

Training

python train.py -s <path to dataset> --eval # Train with train/test split

Additional Command Line Arguments for train.py

  • blur_sample_num: number of key frames for trajectory time sampling
  • deblur: switch the deblur mode
  • mode: models of camera motion trajectory (i.e. Linear, Spline, Bezier)
  • bezier_order: order of the Bézier curve when use Bézier curve for trajectory modeling

Evaluation

python train.py -s <path to dataset> --eval # Train with train/test split
python render.py -m <path to trained model> # Generate renderings
python metrics.py -m <path to trained model> # Compute error metrics on renderings

Additional Command Line Arguments for render.py

  • optim_pose: optimize the camera pose to align with the dataset

Render Video

python render_video.py -m <path to trained model>

BibTex

@article{Chen_deblurgs2024,
   author       = {Wenbo, Chen and Ligang, Liu},
   title        = {Deblur-GS: 3D Gaussian Splatting from Camera Motion Blurred Images},
   journal      = {Proc. ACM Comput. Graph. Interact. Tech. (Proceedings of I3D 2024)},
   year         = {2024},
   volume       = {7},
   number       = {1},
   numpages     = {13},
   location     = {Philadelphia, PA, USA},
   url          = {http://doi.acm.org/10.1145/3651301},
   doi          = {10.1145/3651301},
   publisher    = {ACM Press},
   address      = {New York, NY, USA},
}

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