DyBluRF: A Novel Dynamic Deblurring Neural Radiance Field for Blurry Monocular Video

核心概念

DyBluRF is a novel dynamic deblurring neural radiance field framework that can effectively render sharp novel spatio-temporal views from blurry monocular videos by jointly reconstructing dynamic 3D scenes and predicting latent sharp rays.

要約

The paper proposes a novel dynamic deblurring neural radiance field framework called DyBluRF for blurry monocular video. It consists of two main stages:

Base Ray Initialization (BRI) Stage:

Coarsely reconstructs dynamic 3D scenes and jointly initializes the base rays using the inaccurate camera poses from the given blurry frames.
Employs an interleave optimization strategy to simultaneously advance the learning of 3D dynamic reconstruction and initialize the base rays for deblurring.

Motion Decomposition-based Deblurring (MDD) Stage:

Introduces a novel Incremental Latent Sharp-rays Prediction (ILSP) approach to effectively handle the blurriness due to global camera and local object motions.
Proposes two loss functions: Unsupervised Staticness Maximization Loss and Local Geometry Variance Distillation to optimize the sharp radiance fields without any mask supervision.
Experiments show that DyBluRF significantly outperforms various SOTA methods in dynamic deblurring novel view synthesis, both qualitatively and quantitatively. It also demonstrates robust performance against different degrees of blurriness in the input video.

統計

"We synthesize a new blurry version of iPhone dataset [19], called Blurry iPhone dataset, which consists of synthetic blurry video frames {Bt}Nf
t=1 along with corresponding inaccurate camera poses {˜Pt}Nf
t=1 for training."
"To synthesize realistic and natural blur during the camera exposure in the training video frames, we follow the approach of representative synthesized 2D video deblurring datasets [34, 35]."

引用

"To overcome these issues, distinguished from prior static deblur NeRFs [24, 32, 57] which directly predict latent rays from imprecise input cameras, we newly propose the BRI stage (Sec. 3.3) to provide a stronger foundation for the accurate prediction of latent sharp rays."
"To overcome these limitations, we newly introduce the MDD stage (Sec. 3.4), with a novel Incremental Latent Sharp-rays Prediction (ILSP) method which can effectively synthesize the physical blur process considering global camera and local object motions in a progressive manner along temporal axis."

抽出されたキーインサイト

DyBluRF

by Minh-Quan Vi... 場所 arxiv.org 04-01-2024

https://arxiv.org/pdf/2312.13528.pdf

深掘り質問

How can the proposed DyBluRF framework be extended to handle more complex dynamic scenes, such as those with non-rigid deformations or occlusions

To extend the DyBluRF framework to handle more complex dynamic scenes with non-rigid deformations or occlusions, several modifications and enhancements can be implemented. One approach is to incorporate additional modules for motion estimation and segmentation to identify and track non-rigid objects or deformations in the scene. By integrating techniques like optical flow estimation or instance segmentation, DyBluRF can adaptively predict latent rays for dynamic objects with varying shapes and movements. Furthermore, introducing deformable neural radiance fields or deformable convolutional layers can enable the model to capture the non-rigid deformations more effectively. By dynamically adjusting the deformation parameters based on the motion cues extracted from the scene, DyBluRF can enhance its ability to handle complex dynamic scenes with non-rigid deformations and occlusions.

What other types of motion blur, beyond camera and object motion, could the DyBluRF framework be adapted to handle, such as atmospheric distortions or lens aberrations

The DyBluRF framework can be adapted to handle various types of motion blur beyond camera and object motion, such as atmospheric distortions or lens aberrations, by incorporating specific modules and mechanisms. For atmospheric distortions, techniques like haze removal or atmospheric scattering models can be integrated into DyBluRF to account for the impact of atmospheric conditions on the captured frames. By incorporating atmospheric scattering models or dehazing algorithms, DyBluRF can effectively compensate for the atmospheric distortions and enhance the quality of the synthesized views. Similarly, for handling lens aberrations, the framework can include lens distortion correction algorithms or aberration modeling techniques to mitigate the effects of lens imperfections on the captured frames. By incorporating these additional modules, DyBluRF can adapt to a wider range of motion blur scenarios and improve its performance in challenging environmental conditions.

How could the DyBluRF framework be integrated with other video processing tasks, such as video segmentation or video compression, to enable more comprehensive video understanding and manipulation capabilities

Integrating the DyBluRF framework with other video processing tasks, such as video segmentation or video compression, can enhance its capabilities for comprehensive video understanding and manipulation. By incorporating video segmentation algorithms, DyBluRF can segment the dynamic scenes into different regions or objects, enabling more targeted deblurring and rendering for specific elements in the scene. This integration can improve the overall quality and accuracy of the synthesized views by focusing on individual objects or regions during the rendering process. Additionally, by integrating video compression techniques, DyBluRF can optimize the storage and transmission of the synthesized videos, reducing the file size while maintaining high visual quality. By leveraging video compression algorithms, DyBluRF can efficiently encode the synthesized views and enhance the overall video processing pipeline for various applications, including streaming, storage, and transmission.