toplogo
Resources
Sign In

Recovering Sharp Neural Radiance Fields from Motion-Blurred Images and Events


Core Concepts
The proposed Ev-DeblurNeRF method combines blurry images and events to recover sharp neural radiance fields, outperforming previous event-based and image-only deblur NeRF approaches.
Abstract
The paper presents Ev-DeblurNeRF, a novel approach for recovering sharp neural radiance fields (NeRFs) from motion-blurred images and events. The key highlights are: Ev-DeblurNeRF leverages both model-based priors and learning-based modules to address the challenge of recovering sharp NeRFs from blurry images. It explicitly models the blur formation process, exploiting the event double integral as an additional model-based prior. Additionally, it employs an end-to-end learnable event camera response function to adapt to non-idealities in the real event-camera sensor. Ev-DeblurNeRF outperforms existing deblur NeRF methods that use only frames as well as those that combine frames and events, achieving +6.13dB and +2.48dB higher PSNR, respectively, on real-world data. The method is also 6.9x faster to train compared to the previous event-based deblur NeRF approach. The authors introduce two new datasets, one synthetic and one real-world, featuring precise ground truth poses for accurate quality assessment.
Stats
"We show, on synthetic and real data, that the proposed approach outperforms existing deblur NeRFs that use only frames as well as those that combine frames and events by +6.13dB and +2.48dB, respectively." "Ev-DeblurNeRF recovers radiance fields that are +6.13dB more accurate than image-only baselines, and +2.48dB more accurate than NeRFs exploiting both images and events on real data."
Quotes
"Ev-DeblurNeRF combines blurry images and events to recover sharp NeRFs. A motion-aware NeRF recovers camera motion and a learnable event camera response function models real camera's non-idealities, enabling high-quality reconstructions." "We show, on synthetic and real data, that the proposed approach outperforms existing deblur NeRFs that use only frames as well as those that combine frames and events by +6.13dB and +2.48dB, respectively."

Deeper Inquiries

How could the proposed Ev-DeblurNeRF approach be extended to handle more complex scene dynamics, such as deformable or articulated objects

To extend the Ev-DeblurNeRF approach to handle more complex scene dynamics involving deformable or articulated objects, several modifications and enhancements could be implemented. One approach could involve incorporating additional modules or networks specialized in handling deformable structures. For example, introducing a deformable NeRF module that can adapt to the changing shapes of objects in the scene could be beneficial. This module could utilize techniques from deformable object reconstruction and tracking to improve the reconstruction of deformable or articulated objects. Additionally, integrating motion prediction models specifically designed for deformable objects could help in capturing their movements accurately. By combining these specialized modules with the existing architecture of Ev-DeblurNeRF, the system could effectively handle more complex scene dynamics involving deformable or articulated objects.

What are the potential limitations of the current event-based camera technology, and how could future advancements in event sensors impact the performance of Ev-DeblurNeRF

The current event-based camera technology has certain limitations that could impact the performance of Ev-DeblurNeRF. One limitation is the spatial resolution of event cameras, which is typically lower than traditional frame-based cameras. This lower resolution can result in reduced image quality and detail, which may affect the accuracy of the reconstructed radiance fields in Ev-DeblurNeRF. Another limitation is the processing overhead associated with event data, as event cameras generate a large amount of data that needs to be efficiently processed and utilized in real-time applications. Future advancements in event sensors could address these limitations by improving the spatial resolution of event cameras, enhancing their sensitivity to light, and optimizing the data processing algorithms. Higher resolution event cameras with improved sensitivity and faster processing capabilities could significantly enhance the performance of Ev-DeblurNeRF by providing more detailed and accurate event data for reconstruction.

Could the learnable event camera response function in Ev-DeblurNeRF be leveraged to enable joint calibration of the event and standard cameras, improving the overall system's robustness

The learnable event camera response function in Ev-DeblurNeRF could indeed be leveraged to enable joint calibration of event and standard cameras, thereby improving the overall system's robustness. By using the learnable event camera response function to calibrate the event camera's response to color changes and brightness variations, the system could achieve better alignment and synchronization between the event and standard cameras. This calibration process could involve adjusting the event camera's response function based on the output of the standard camera, ensuring consistency in color representation and brightness changes between the two modalities. By jointly calibrating the event and standard cameras using the learnable response function, Ev-DeblurNeRF could enhance the accuracy and reliability of the reconstruction process, especially in scenarios where both types of cameras are used simultaneously.
0