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Event cameras, mimicking the human retina, capture brightness changes with unparalleled temporal resolution and dynamic range. Integrating events into intensities poses a highly ill-posed challenge, marred by initial condition ambiguities.…

Computer Vision and Pattern Recognition · Computer Science 2024-07-12 Jinxiu Liang , Bohan Yu , Yixin Yang , Yiming Han , Boxin Shi

Event cameras or dynamic vision sensors (DVS) record asynchronous response to brightness changes instead of conventional intensity frames, and feature ultra-high sensitivity at low bandwidth. The new mechanism demonstrates great advantages…

Computer Vision and Pattern Recognition · Computer Science 2024-01-09 Bo Zhang , Yuqi Han , Jinli Suo , Qionghai Dai

Video Frame Interpolation (VFI) is a fundamental yet challenging task in computer vision, particularly under conditions involving large motion, occlusion, and lighting variation. Recent advancements in event cameras have opened up new…

Computer Vision and Pattern Recognition · Computer Science 2025-05-14 Hanle Zheng , Xujie Han , Zegang Peng , Shangbin Zhang , Guangxun Du , Zhuo Zou , Xilin Wang , Jibin Wu , Hao Guo , Lei Deng

Video frame interpolation (VFI) in scenarios with large motion remains challenging due to motion ambiguity between frames. While event cameras can capture high temporal resolution motion information, existing event-based VFI methods…

Computer Vision and Pattern Recognition · Computer Science 2025-03-27 Ziran Zhang , Xiaohui Li , Yihao Liu , Yujin Wang , Yueting Chen , Tianfan Xue , Shi Guo

The bio-inspired event cameras or dynamic vision sensors are capable of asynchronously capturing per-pixel brightness changes (called event-streams) in high temporal resolution and high dynamic range. However, the non-structural…

Computer Vision and Pattern Recognition · Computer Science 2024-12-12 Qiang Qu , Yiran Shen , Xiaoming Chen , Yuk Ying Chung , Tongliang Liu

Event cameras excel at high-speed, low-power, and high-dynamic-range scene perception. However, as they fundamentally record only relative intensity changes rather than absolute intensity, the resulting data streams suffer from a…

Computer Vision and Pattern Recognition · Computer Science 2026-05-08 Gang Xu , Zhiyu Zhu , Junhui Hou

Event cameras are novel sensors that report brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. They offer significant advantages with respect to conventional cameras: high temporal resolution,…

Computer Vision and Pattern Recognition · Computer Science 2019-06-19 Henri Rebecq , René Ranftl , Vladlen Koltun , Davide Scaramuzza

We present EvDNeRF, a pipeline for generating event data and training an event-based dynamic NeRF, for the purpose of faithfully reconstructing eventstreams on scenes with rigid and non-rigid deformations that may be too fast to capture…

Computer Vision and Pattern Recognition · Computer Science 2023-12-08 Anish Bhattacharya , Ratnesh Madaan , Fernando Cladera , Sai Vemprala , Rogerio Bonatti , Kostas Daniilidis , Ashish Kapoor , Vijay Kumar , Nikolai Matni , Jayesh K. Gupta

The development of video diffusion models unveils a significant challenge: the substantial computational demands. To mitigate this challenge, we note that the reverse process of diffusion exhibits an inherent entropy-reducing nature. Given…

Computer Vision and Pattern Recognition · Computer Science 2025-03-13 Lingmin Ran , Mike Zheng Shou

Event cameras capture changes of illumination in the observed scene rather than accumulating light to create images. Thus, they allow for applications under high-speed motion and complex lighting conditions, where traditional framebased…

Computer Vision and Pattern Recognition · Computer Science 2021-12-21 Vincent Brebion , Julien Moreau , Franck Davoine

Event-based cameras measure intensity changes (called `events') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the `active pixel sensor' (APS), the `Dynamic and Active-pixel Vision Sensor'…

Computer Vision and Pattern Recognition · Computer Science 2020-11-13 Liyuan Pan , Richard Hartley , Cedric Scheerlinck , Miaomiao Liu , Xin Yu , Yuchao Dai

With the rapid development of deep learning, video deraining has experienced significant progress. However, existing video deraining pipelines cannot achieve satisfying performance for scenes with rain layers of complex spatio-temporal…

Computer Vision and Pattern Recognition · Computer Science 2023-10-02 Yueyi Zhang , Jin Wang , Wenming Weng , Xiaoyan Sun , Zhiwei Xiong

Most of the artificial lights fluctuate in response to the grid's alternating current and exhibit subtle variations in terms of both intensity and spectrum, providing the potential to estimate the Electric Network Frequency (ENF) from…

Image and Video Processing · Electrical Eng. & Systems 2023-05-05 Lexuan Xu , Guang Hua , Haijian Zhang , Lei Yu , Ning Qiao

Video Frame Interpolation aims to recover realistic missing frames between observed frames, generating a high-frame-rate video from a low-frame-rate video. However, without additional guidance, the large motion between frames makes this…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Jingxi Chen , Brandon Y. Feng , Haoming Cai , Tianfu Wang , Levi Burner , Dehao Yuan , Cornelia Fermuller , Christopher A. Metzler , Yiannis Aloimonos

We present Ev-NeRF, a Neural Radiance Field derived from event data. While event cameras can measure subtle brightness changes in high frame rates, the measurements in low lighting or extreme motion suffer from significant domain…

Computer Vision and Pattern Recognition · Computer Science 2023-03-07 Inwoo Hwang , Junho Kim , Young Min Kim

Continuous video monitoring in surveillance, robotics, and wearable systems faces a fundamental power constraint: conventional RGB cameras consume substantial energy through fixed-rate capture. Event cameras offer sparse, motion-driven…

Computer Vision and Pattern Recognition · Computer Science 2025-12-08 Dmitrii Torbunov , Onur Okuducu , Yi Huang , Odera Dim , Rebecca Coles , Yonggang Cui , Yihui Ren

Event cameras asynchronously capture brightness changes with microsecond latency, offering exceptional temporal precision but suffering from severe noise and signal inconsistencies. Unlike conventional signals, events carry state…

Computer Vision and Pattern Recognition · Computer Science 2025-11-21 Jinze Chen , Wei Zhai , Yang Cao , Bin Li , Zheng-Jun Zha

Event cameras are a new type of vision sensor that incorporates asynchronous and independent pixels, offering advantages over traditional frame-based cameras such as high dynamic range and minimal motion blur. However, their output is not…

Computer Vision and Pattern Recognition · Computer Science 2024-04-08 Burak Ercan , Onur Eker , Aykut Erdem , Erkut Erdem

Event cameras offer many advantages over standard cameras due to their distinctive principle of operation: low power, low latency, high temporal resolution and high dynamic range. Nonetheless, the success of many downstream visual…

Computer Vision and Pattern Recognition · Computer Science 2023-09-18 Weng Fei Low , Gim Hee Lee

Diffusion models, as a type of generative model, have achieved impressive results in generating images and videos conditioned on textual conditions. However, the generation process of diffusion models involves denoising dozens of steps to…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Hui Zhang , Zuxuan Wu , Zhen Xing , Jie Shao , Yu-Gang Jiang
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