Recovering Neural Radiance Fields from a Single Blurry Image and Event Stream

Core Concepts

A method to jointly recover the underlying 3D scene representation and camera motion trajectory from a single blurry image and its corresponding event stream.

Abstract

The paper presents a novel method to jointly recover the neural radiance fields (NeRF) and the camera motion trajectory from a single blurry image and its corresponding event stream.
Key highlights:

The method represents the 3D scene using a multi-layer perceptron (MLP) and models the continuous camera motion trajectory using a cubic B-Spline in SE(3) space.
Both the blurry image and the accumulated events within a time interval can be synthesized from the NeRF representation and the camera poses.
The NeRF and camera motion are jointly optimized by minimizing the difference between the synthesized data and the real measurements, without any prior knowledge of camera poses.
Extensive experiments on synthetic and real-world datasets demonstrate the superior performance of the proposed method compared to prior single-image deblurring, event-enhanced deblurring, and NeRF-based deblurring methods.
The method can effectively recover high-quality latent sharp images and high frame-rate videos from a single blurry image, without any generalization issues.

Stats

The blurry image is formed by collecting photons during the exposure time, which can be modeled as the average of a sequence of virtual sharp images.
The event stream is accumulated within a time interval to an image, which represents the brightness change over time.

Quotes

"Given a single blurry image and its corresponding event stream, BeNeRF can synthesize high-quality novel images along the camera trajectory, recovering a sharp and coherent video from the single blurry image."
"Our method can jointly learn both the implicit scene representation and the camera motion by minimizing the differences between the synthesized data and the real measurements without any prior knowledge of camera poses."

Key Insights Distilled From

BeNeRF: Neural Radiance Fields from a Single Blurry Image and Event Stream

by Wenpu Li, Pi... at arxiv.org 09-12-2024

https://arxiv.org/pdf/2407.02174.pdf

Deeper Inquiries

How can the proposed method be extended to handle more complex scene geometries or dynamic scenes?

The proposed method, BeNeRF, can be extended to handle more complex scene geometries or dynamic scenes by incorporating several enhancements. Firstly, integrating multi-scale representations could allow the model to capture finer details in intricate geometries. This could involve using hierarchical neural representations that adaptively refine the scene representation based on the complexity of the geometry being rendered.
Secondly, the method could benefit from incorporating temporal coherence for dynamic scenes. By leveraging recurrent neural networks (RNNs) or temporal convolutional networks (TCNs), the model could learn to predict motion patterns over time, allowing it to better handle scenes with moving objects or changing environments. This would involve extending the event stream processing to account for dynamic changes, potentially using a more sophisticated event accumulation strategy that considers the temporal dynamics of the scene.
Additionally, the use of more advanced motion modeling techniques, such as neural splines or learned motion priors, could enhance the ability to represent complex camera trajectories and scene dynamics. These approaches could provide a more flexible framework for capturing non-linear motion and interactions between multiple moving objects, thereby improving the overall robustness and accuracy of the 3D reconstruction.

What are the potential limitations of the cubic B-Spline representation for modeling camera motion, and how could alternative representations be explored?

While the cubic B-Spline representation offers a smooth and differentiable way to model camera motion in SE(3) space, it has certain limitations. One potential limitation is its reliance on a fixed number of control points, which may not adequately capture highly dynamic or erratic camera movements. If the camera motion is too complex, the cubic B-Spline may struggle to represent the trajectory accurately, leading to artifacts in the synthesized images.
Another limitation is that cubic B-Splines can introduce oversmoothing, which may not be suitable for scenes with rapid changes in motion or abrupt camera movements. This could result in a loss of detail in the reconstructed scene, particularly in scenarios where precise motion tracking is critical.
To address these limitations, alternative representations could be explored. For instance, using piecewise linear splines or higher-order splines could provide more flexibility in capturing complex trajectories. Additionally, employing learned representations, such as neural networks that predict camera poses based on the event stream, could allow for more adaptive modeling of camera motion. These networks could be trained on diverse motion patterns, enabling them to generalize better to various scenarios.

Can the insights from this work be applied to other computer vision tasks beyond 3D reconstruction, such as object tracking or augmented reality applications?

Yes, the insights from BeNeRF can be effectively applied to other computer vision tasks beyond 3D reconstruction, including object tracking and augmented reality (AR) applications. The ability to recover high-quality latent sharp images from a single blurry image and an event stream can significantly enhance object tracking performance. By accurately estimating the camera motion and scene representation, the method can provide robust tracking of objects in dynamic environments, even under challenging conditions such as motion blur.
In augmented reality applications, the capability to synthesize novel views from a single image can facilitate the seamless integration of virtual objects into real-world scenes. The learned neural radiance fields can be utilized to render virtual objects with realistic lighting and occlusion effects, enhancing the immersive experience of AR applications. Furthermore, the event stream processing can improve the responsiveness of AR systems by providing high temporal resolution, allowing for real-time adjustments to the virtual content based on user interactions or changes in the environment.
Overall, the methodologies and insights from this work can contribute to advancements in various computer vision domains, enabling more robust and efficient solutions for real-time applications.