Mitigating Motion Blur in Neural Radiance Fields with Events and Frames

• URL: http://arxiv.org/abs/2403.19780v2
• Date: Mon, 3 Jun 2024 17:56:14 GMT
• Title: Mitigating Motion Blur in Neural Radiance Fields with Events and Frames
• Authors: Marco Cannici, Davide Scaramuzza,
• Abstract summary: We propose a novel approach to enhance NeRF reconstructions under camera motion by fusing frames and events. We explicitly model the blur formation process, exploiting the event double integral as an additional model-based prior. We show, on synthetic and real data, that the proposed approach outperforms existing deblur NeRFs that use only frames.
• Score: 21.052912896866953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Radiance Fields (NeRFs) have shown great potential in novel view synthesis. However, they struggle to render sharp images when the data used for training is affected by motion blur. On the other hand, event cameras excel in dynamic scenes as they measure brightness changes with microsecond resolution and are thus only marginally affected by blur. Recent methods attempt to enhance NeRF reconstructions under camera motion by fusing frames and events. However, they face challenges in recovering accurate color content or constrain the NeRF to a set of predefined camera poses, harming reconstruction quality in challenging conditions. This paper proposes a novel formulation addressing these issues by leveraging both model- and learning-based modules. We explicitly model the blur formation process, exploiting the event double integral as an additional model-based prior. Additionally, we model the event-pixel response using an end-to-end learnable response function, allowing our method to adapt to non-idealities in the real event-camera sensor. 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.

Related papers

• Deblur e-NeRF: NeRF from Motion-Blurred Events under High-speed or Low-light Conditions [56.84882059011291]
  We propose Deblur e-NeRF, a novel method to reconstruct blur-minimal NeRFs from motion-red events. We also introduce a novel threshold-normalized total variation loss to improve the regularization of large textureless patches.
  arXiv  Detail & Related papers  (2024-09-26T15:57:20Z)
• Deblurring Neural Radiance Fields with Event-driven Bundle Adjustment [23.15130387716121]
  We propose Bundle Adjustment for Deblurring Neural Radiance Fields (EBAD-NeRF) to jointly optimize the learnable poses and NeRF parameters. EBAD-NeRF can obtain accurate camera trajectory during the exposure time and learn a sharper 3D representations compared to prior works.
  arXiv  Detail & Related papers  (2024-06-20T14:33:51Z)
• Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uniform Motion [67.15935067326662]
  Event cameras offer low power, low latency, high temporal resolution and high dynamic range. NeRF is seen as the leading candidate for efficient and effective scene representation. We propose Robust e-NeRF, a novel method to directly and robustly reconstruct NeRFs from moving event cameras.
  arXiv  Detail & Related papers  (2023-09-15T17:52:08Z)
• Deformable Neural Radiance Fields using RGB and Event Cameras [65.40527279809474]
  We develop a novel method to model the deformable neural radiance fields using RGB and event cameras. The proposed method uses the asynchronous stream of events and sparse RGB frames. Experiments conducted on both realistically rendered graphics and real-world datasets demonstrate a significant benefit of the proposed method.
  arXiv  Detail & Related papers  (2023-09-15T14:19:36Z)
• BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields [9.744593647024253]
  We present a novel bundle adjusted deblur Neural Radiance Fields (BAD-NeRF) BAD-NeRF can be robust to severe motion blurred images and inaccurate camera poses. Our approach models the physical image formation process of a motion blurred image, and jointly learns the parameters of NeRF.
  arXiv  Detail & Related papers  (2022-11-23T10:53:37Z)
• E-NeRF: Neural Radiance Fields from a Moving Event Camera [83.91656576631031]
  Estimating neural radiance fields (NeRFs) from ideal images has been extensively studied in the computer vision community. We present E-NeRF, the first method which estimates a volumetric scene representation in the form of a NeRF from a fast-moving event camera.
  arXiv  Detail & Related papers  (2022-08-24T04:53:32Z)
• EventNeRF: Neural Radiance Fields from a Single Colour Event Camera [81.19234142730326]
  This paper proposes the first approach for 3D-consistent, dense and novel view synthesis using just a single colour event stream as input. At its core is a neural radiance field trained entirely in a self-supervised manner from events while preserving the original resolution of the colour event channels. We evaluate our method qualitatively and numerically on several challenging synthetic and real scenes and show that it produces significantly denser and more visually appealing renderings.
  arXiv  Detail & Related papers  (2022-06-23T17:59:53Z)
• Reducing the Sim-to-Real Gap for Event Cameras [64.89183456212069]
  Event cameras are paradigm-shifting novel sensors that report asynchronous, per-pixel brightness changes called 'events' with unparalleled low latency. Recent work has demonstrated impressive results using Convolutional Neural Networks (CNNs) for video reconstruction and optic flow with events. We present strategies for improving training data for event based CNNs that result in 20-40% boost in performance of existing video reconstruction networks.
  arXiv  Detail & Related papers  (2020-03-20T02:44:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.