Gaussian Splatting on the Move

Blur and Rolling Shutter Compensation for Natural Camera Motion

Otto Seiskari1, Jerry Ylilammi1, Valtteri Kaatrasalo1, Pekka Rantalankila1,
Matias Turkulainen2, Juho Kannala1,3, Esa Rahtu1,4 Arno Solin1,3
1Spectacular AI, 2ETH Zürich, 3Aalto University, 4Tampere University
Paper Video Code Data

T L D R :

Crisp Gaussian Splatting reconstructions from blurry and wobbly smartphone captures.

Motion blur and rolling shutter compensation for 3DGS using VIO IMU data, pose refinement, and a differentiable image formation model. Results demonstrated on synthetic and casually captured smartphone data.

Motion blur

Rolling shutter

First row: demonstrates simulated motion blur and rolling shutter effects as a post-processing step for a 3DGS model. Effects are exaggerated for visual demonstration purposes.

Second row: shows 3DGS renders from training data affected by motion blur and rolling shutter effects. We compare against the baseline Splatfacto and our model.

Abstract

High-quality scene reconstruction and novel view synthesis based on Gaussian Splatting (3DGS) typically require steady, high-quality photographs, often impractical to capture with handheld cameras. We present a method that adapts to camera motion and allows high-quality scene reconstruction with handheld video data suffering from motion blur and rolling shutter distortion. Our approach is based on detailed modelling of the physical image formation process and utilizes velocities estimated using visual-inertial odometry (VIO). Camera poses are considered non-static during the exposure time of a single image frame and camera poses are further optimized in the reconstruction process. We formulate a differentiable rendering pipeline that leverages screen space approximation to efficiently incorporate rolling-shutter and motion blur effects into the 3DGS framework. Our results with both synthetic and real data demonstrate superior performance in mitigating camera motion over existing methods, thereby advancing 3DGS in naturalistic settings.

Real data

Casual iPhone 15 captures with moderate motion blur. Comparing Splatfacto to our method (using COLMAP poses)

Samsung Galaxy S20, an Android phone with a typical long rolling shutter readout time. Comparing Splatfacto with COLMAP poses to our method with rolling shutter compensated pose optimization and motion blur compensation.

Synthetic data

Our results on a re-rendered and extended version of the Deblur-NeRF dataset (Zenodo link)

Motion blur

Rolling shutter

Pose noise

BibTeX

@misc{seiskari2024gaussian,
      title={Gaussian Splatting on the Move: Blur and Rolling Shutter Compensation for Natural Camera Motion}, 
      author={Otto Seiskari and Jerry Ylilammi and Valtteri Kaatrasalo and Pekka Rantalankila and Matias Turkulainen and Juho Kannala and Esa Rahtu and Arno Solin},
      year={2024},
      eprint={2403.13327},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}