The paper "Mitigating Motion Blur in Neural Radiance Fields with Events and Frames" by Marco Cannici and Davide Scaramuzza addresses the challenge of rendering sharp images using Neural Radiance Fields (NeRFs) when training data is affected by motion blur. The authors propose a novel approach called Ev-DebluNeRF, which combines blurry images and events to recover sharp NeRFs. The method explicitly models the blur formation process using the event double integral (EDI) as a model-based prior and employs an end-to-end learnable response function to adapt to real event-camera sensor non-idealities. The proposed approach outperforms existing deblur NeRFs that use only frames or combine frames and events, achieving +6.13dB and +2.48dB improvements in PSNR, respectively, on synthetic and real data. The paper also introduces two new datasets, one simulated and one collected using a Color-DAVIS event camera, to evaluate the method's performance. The contributions of the work include a novel approach for recovering sharp NeRFs in the presence of motion blur, a NeRF formulation that is more accurate and faster to train, and the introduction of new datasets for quality assessment.The paper "Mitigating Motion Blur in Neural Radiance Fields with Events and Frames" by Marco Cannici and Davide Scaramuzza addresses the challenge of rendering sharp images using Neural Radiance Fields (NeRFs) when training data is affected by motion blur. The authors propose a novel approach called Ev-DebluNeRF, which combines blurry images and events to recover sharp NeRFs. The method explicitly models the blur formation process using the event double integral (EDI) as a model-based prior and employs an end-to-end learnable response function to adapt to real event-camera sensor non-idealities. The proposed approach outperforms existing deblur NeRFs that use only frames or combine frames and events, achieving +6.13dB and +2.48dB improvements in PSNR, respectively, on synthetic and real data. The paper also introduces two new datasets, one simulated and one collected using a Color-DAVIS event camera, to evaluate the method's performance. The contributions of the work include a novel approach for recovering sharp NeRFs in the presence of motion blur, a NeRF formulation that is more accurate and faster to train, and the introduction of new datasets for quality assessment.