{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/e2nerf-event-enhanced-neural-radiance-fields","title":"E2NeRF: Event Enhanced Neural Radiance Fields from Blurry Images","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Yunshan Qi","Lin Zhu","Yu Zhang","Jia Li"],"abstract":"    Neural Radiance Fields (NeRF) achieves impressive ren-dering performance by learning volumetric 3D representation from several images of different views. However, it is difficult to reconstruct a sharp NeRF from blurry input as often occurred in the wild. To solve this problem, we propose a novel Event-Enhanced NeRF (E2NeRF) by utilizing the combination data of a bio-inspired event camera and a standard RGB camera. To effectively introduce event stream into the learning process of neural volumetric representation, we propose a blur rendering loss and an event rendering loss, which guide the network via modelling real blur process and event generation process, respectively. Moreover, a camera pose estimation framework for real-world data is built with the guidance of event stream to generalize the method to practical applications. In contrast to previous image-based or event-based NeRF, our framework effectively utilizes the internal relationship between events and images. As a result, E2NeRF not only achieves image deblurring but also achieves high-quality novel view image generation. Extensive experiments on both synthetic data and real-world data demonstrate that E2NeRF can effectively learn a sharp NeRF from blurry images, especially in complex and low-light scenes. Our code and datasets are publicly available at https://github.com/iCVTEAM/E2NeRF.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Qi_E2NeRF_Event_Enhanced_Neural_Radiance_Fields_from_Blurry_Images_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Qi_E2NeRF_Event_Enhanced_Neural_Radiance_Fields_from_Blurry_Images_ICCV_2023_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"e2nerf-event-enhanced-neural-radiance-fields","repo_url":"https://github.com/icvteam/e2nerf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"camera-pose-estimation","task_name":"Camera Pose Estimation"},{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"image-deblurring","task_name":"Image Deblurring"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"nerf","task_name":"NeRF"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}