Sparse-DeRF: Deblurred Neural Radiance Fields from Sparse View | JoVE Visualize

Area of Science:

Computer Vision
Computer Graphics
Machine Learning

Background:

Neural Radiance Fields (NeRF) typically require numerous images for accurate scene reconstruction.
Existing deblurred NeRF (DeRF) methods are impractical for real-world scenarios with few available images.
Constructing DeRF from sparse views presents significant challenges due to simultaneous blur kernel and NeRF optimization.

Purpose of the Study:

To develop a method for constructing deblurred neural radiance fields (DeRF) from a limited number of blurry images (sparse-view).
To address the inherent complexities and overfitting artifacts associated with joint optimization of blur kernels and NeRF from sparse data.
To enhance the quality and practicality of DeRF in real-world applications where image availability is restricted.

Main Methods:

Introduced Sparse-DeRF, a novel approach to regularize the joint optimization of blur kernels and NeRF from sparse views.
Implemented three key regularization components: surface smoothness, modulated gradient scaling, and perceptual distillation.
Surface smoothness leverages statistical tendencies for accurate scene structure prediction.
Modulated gradient scaling adjusts backpropagated gradients based on scene object arrangements.
Perceptual distillation overcomes multi-view inconsistency and compensates for missing clean image information.

Main Results:

Sparse-DeRF successfully regularizes the complex joint optimization problem.
Demonstrated alleviated overfitting artifacts and enhanced radiance field quality.
Achieved effective DeRF construction from as few as 2, 4, and 6 blurry views.
Extensive quantitative and qualitative experimental results validate the method's effectiveness.

Conclusions:

Sparse-DeRF offers a practical solution for constructing high-quality deblurred neural radiance fields from sparse-view blurry images.
The proposed regularization techniques effectively address the challenges of limited multi-view information.
This work significantly advances the applicability of DeRF in real-world scenarios with image constraints.