Gaussian Splatting for Motion Blur and Rolling Shutter Compensation

Core Concepts

Efficiently implement motion blur and rolling shutter effects in 3D Gaussian Splatting for improved scene reconstruction.

Abstract

The article introduces a method for compensating motion blur and rolling shutter distortion in handheld video data using Gaussian Splatting. It details the physical image formation process, velocity estimation, rendering pipeline, and regularization strategies. Results show superior performance over existing methods in both synthetic and real data experiments.

Introduction

Recent advancements in novel view synthesis.
Limitations of high-quality still photographs.
Introduction of Neural Radiance Fields (NeRF) and Gaussian Splatting (3DGS).

Motion Blur Compensation

Challenges with capturing moving sensor data.
Existing methods for motion blur compensation.
Utilizing 3D novel view synthesis methods like NeRF and 3DGS.

Rolling Shutter Compensation

Effects of rolling shutter distortion on fast-moving scenes.
Incorporating rolling shutter correction into the 3DGS framework.
Comparison with traditional methods like Structure-from-Motion (SfM).

Screen Space Approximation

Decomposing the rendering model into two stages.
Transforming Gaussian parameters to reflect camera motion.
Rasterization process independent of camera pose.

Pose Optimization

Approximating gradients with respect to camera pose components.
Leveraging pose optimization for better registration in the presence of rolling shutter effects.

Regularization Strategies

Underestimating exposure time and adding noise vectors for robustness.

Experiments

Evaluation on synthetic data sets with different variants.
Implementation details using NerfStudio and gsplat software packages.

Smartphone Data Evaluation

Real-world data evaluation on smartphones with varying rolling shutter readout times.

Timing Tests

Training wall clock time comparison between baseline and motion blur compensation.

Stats

Our results consistently outperform baselines across all scenarios, demonstrating effectiveness in compensating for blurring and RS effects.

Quotes

"We present a method that adapts to camera motion..."
"Our results demonstrate superior performance..."

Key Insights Distilled From

Gaussian Splatting on the Move

by Otto Seiskar... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13327.pdf

Deeper Inquiries

How can this method be applied to other fields beyond computer vision

This method of incorporating motion blur compensation directly into the 3D model generation can be applied to various fields beyond computer vision. One potential application is in robotics, where accurate scene reconstruction is crucial for tasks such as autonomous navigation and object manipulation. By improving the quality of reconstructions from moving cameras, robots can better understand their environment and make more informed decisions. Additionally, this approach could be valuable in augmented reality (AR) applications, where realistic virtual objects need to be seamlessly integrated into real-world scenes despite camera motion-induced blur.

What are potential drawbacks or limitations of incorporating motion blur compensation directly into the 3D model generation

One potential drawback of incorporating motion blur compensation directly into the 3D model generation is the computational complexity it introduces. Calculating and optimizing for motion blur effects during rendering can significantly increase processing time and resource requirements. This may limit real-time applications or require powerful hardware to achieve acceptable performance levels. Additionally, inaccuracies in estimating velocity vectors or modeling complex motion trajectories could lead to artifacts or distortions in the reconstructed scenes.

How might refining VIO-based velocity estimates further enhance reconstruction results

Refining Visual-Inertial Odometry (VIO)-based velocity estimates could further enhance reconstruction results by providing more accurate information about camera movement during image capture. Improved velocity estimates would lead to better modeling of motion blur effects and rolling shutter distortion, resulting in sharper reconstructions with fewer artifacts. By fine-tuning VIO algorithms to provide precise velocity data that aligns closely with actual camera movements, the overall quality and accuracy of 3D scene reconstructions can be significantly enhanced.