SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields

Conceptos Básicos

The author proposes SMURF, a novel approach utilizing Neural ODE to model continuous camera motion for reconstructing sharp 3D scenes from motion-blurred images. The core idea is the Continuous Motion Blurring Kernel (CMBK) designed to handle sequential camera movements accurately.

Resumen

SMURF introduces a unique approach to address the challenge of motion blur in synthesizing 3D scenes. By modeling continuous camera motion with CMBK and leveraging Neural ODE, SMURF demonstrates superior performance quantitatively and qualitatively compared to existing methods. The regularization strategies of residual momentum and output suppression loss further enhance the accuracy of reconstructed scenes.
Key points:

Introduction of SMURF for accurate 3D scene reconstruction from motion-blurred images.
Utilization of CMBK and Neural ODE for modeling continuous camera motion.
Superior performance demonstrated through quantitative metrics like PSNR, SSIM, and LPIPS.
Regularization strategies such as residual momentum and output suppression loss contribute to improved results.

Estadísticas

NeRF integrates the output radiance and volume density along emitted rays.
Deblur-NeRF proposes a method that models blurring kernel by imitating blind image deblurring.
DP-NeRF shows performance improvement but suffers from slow training and rendering speed.
PDRF demonstrates fast training but offers relatively lower performance.

Citas

"Our model rigorously evaluated against benchmark datasets demonstrates state-of-the-art performance both quantitatively and qualitatively."
"SMURF significantly outperforms previous works quantitatively with faster training and rendering."

Ideas clave extraídas de

SMURF

by Jungho Lee,D... a las arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07547.pdf

Consultas más profundas

How does the use of Neural ODE enhance the modeling of continuous camera motion in SMURF

In SMURF, the use of Neural ODE enhances the modeling of continuous camera motion by allowing for a more precise tracking of camera movement paths. Neural ODEs model a parameterized time-continuous latent state and output a unique solution of the integral of continuous dynamics. By embedding information about the initial ray into the latent space and applying Neural ODEs to design a continuous latent space, SMURF can accurately estimate small changes in camera pose over time. This approach ensures that the sequentially computed camera movements exhibit continuity, reflecting the physics inherent in camera motion. The use of Neural ODEs enables SMURF to capture complex or irregular camera movements with greater accuracy compared to traditional methods.

What potential applications beyond 3D scene reconstruction could be explored using the CMBK module

The CMBK module used in SMURF has potential applications beyond 3D scene reconstruction. One possible application could be in video processing tasks such as video deblurring or object tracking where accurate modeling of continuous motion is crucial. By leveraging CMBK to estimate precise and sequential camera motions from blurry images, it could enhance video deblurring algorithms by providing more accurate estimates of motion blur kernels over time. Additionally, CMBK could be utilized in robotics for tasks like visual odometry or autonomous navigation systems where understanding and predicting continuous motion trajectories are essential for efficient and safe operation.

How might advancements in regularization techniques like residual momentum impact future developments in computer vision research

Advancements in regularization techniques like residual momentum can have significant impacts on future developments in computer vision research. Residual momentum helps prevent divergence during optimization processes by ensuring that predicted poses do not deviate significantly from previous poses. This regularization technique promotes stability during training and leads to better convergence towards optimal solutions without overfitting or underfitting issues. In future computer vision research, incorporating advanced regularization techniques like residual momentum can improve model generalization, robustness, and performance across various tasks such as image restoration, object detection, semantic segmentation, and more.