Optimizing Sharp 3D Scenes from Camera Motion Blurred Images using Gaussian Splatting

Core Concepts

A method to optimize sharp 3D Gaussian Splatting scenes from camera motion blurred images, leveraging the fine-detailed reconstruction capability of Gaussian Splatting and addressing the challenge of optimizing from inaccurate initial camera poses.

Abstract

The paper presents DeblurGS, a method for reconstructing sharp 3D scenes from a collection of motion blurred images. The key highlights are:

DeblurGS adopts 3D Gaussian Splatting (3DGS) as the scene representation to achieve photo-realistic recovery of 3D scenes in fine-grained detail.

It jointly optimizes the latent camera motion of blurry images and the sharp 3D Gaussian Splatting scene. Specifically, it reconstructs blurry images by accumulating 3DGS rendered images following the estimated camera motion of training views.

To address the challenge of optimizing from inaccurate initial camera poses obtained through Structure-from-Motion on blurry images, DeblurGS proposes a Gaussian Densification Annealing strategy. This enables stable optimization of the sharp 3D scene even with noisy pose initialization.

DeblurGS also introduces sub-frame alignment parameters to control the discrete sampling intervals on the continuous camera motion trajectory, achieving more accurate blur optimization.

Comprehensive experiments demonstrate that DeblurGS outperforms previous methods in deblurring and novel view synthesis on real-world and synthetic benchmark datasets, as well as field-captured blurry smartphone videos.

Stats

The camera motion blur is generated from the integration of irradiance during camera movement, which can be approximated as a finite sum of N sub-frame images.
The sub-frame alignment parameter ν calibrates each camera pose on the estimated trajectory to be aligned with the latent camera poses at time τi.

Quotes

"We restore a fine-grained sharp scene by leveraging the remarkable reconstruction capability of 3D Gaussian Splatting."
"To address this challenge, we propose DeblurGS, a method to optimize sharp 3D Gaussian Splatting from motion-blurred images, even with the noisy camera pose initialization."
"Comprehensive experiments demonstrate that our DeblurGS achieves state-of-the-art performance in deblurring and novel view synthesis for real-world and synthetic benchmark datasets, as well as field-captured blurry smartphone videos."

Key Insights Distilled From

DeblurGS: Gaussian Splatting for Camera Motion Blur

by Jeongtaek Oh... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11358.pdf

Deeper Inquiries

How can the proposed DeblurGS framework be extended to handle other types of image degradations beyond camera motion blur, such as defocus blur or atmospheric turbulence

The DeblurGS framework can be extended to handle other types of image degradations beyond camera motion blur by adapting the optimization process and loss functions to address specific characteristics of different types of blur. For example, to handle defocus blur, the framework could incorporate depth estimation techniques to better understand the depth of the scene and adjust the rendering accordingly. This could involve optimizing the sharp 3D scene representation based on the estimated depth information to deblur the image effectively. Additionally, for atmospheric turbulence, the framework could integrate motion estimation algorithms to account for the distortion caused by the turbulence and optimize the scene representation accordingly. By incorporating these additional components and adapting the optimization process, DeblurGS can be extended to handle a variety of image degradations beyond camera motion blur.

What are the potential limitations of the Gaussian Splatting representation and how could it be further improved to handle more complex scene geometries and materials

The potential limitations of the Gaussian Splatting representation lie in its ability to handle complex scene geometries and materials. Gaussian Splatting may struggle with highly detailed or intricate scenes that require a more sophisticated representation to capture fine details accurately. To improve the handling of complex scene geometries and materials, the Gaussian Splatting representation could be further enhanced by incorporating adaptive Gaussian primitives that can better capture the nuances of the scene. Additionally, integrating advanced rendering techniques such as ray tracing or Monte Carlo path tracing could enhance the realism and accuracy of the rendered scenes. By improving the representation of scene geometry and materials, Gaussian Splatting can overcome its limitations and handle more complex scenes effectively.

Given the success of DeblurGS in reconstructing sharp 3D scenes from blurry images, how could this approach be leveraged for other applications like computational photography, augmented reality, or robot navigation

The success of DeblurGS in reconstructing sharp 3D scenes from blurry images opens up opportunities for various applications beyond deblurring. In computational photography, the approach could be utilized for image enhancement, super-resolution, and depth estimation tasks to improve the overall quality of images. In augmented reality, DeblurGS could be used for real-time scene reconstruction and rendering, enhancing the visual fidelity of AR experiences. For robot navigation, the framework could assist in creating detailed 3D maps of environments from blurry sensor data, enabling more accurate localization and path planning for robots. By leveraging the capabilities of DeblurGS in different applications, it can contribute to advancements in computational imaging, AR technologies, and robotic systems.