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This paper presents a new method for 3D depth estimation using the output of an asynchronous time driven image sensor. In association with a high speed Digital Light Processing projection system, our method achieves real-time reconstruction…

Computer Vision and Pattern Recognition · Computer Science 2018-11-28 T. Leroux , S. -H. Ieng , R. Benosman

Despite the dynamic development of computer vision algorithms, the implementation of perception and control systems for autonomous vehicles such as drones and self-driving cars still poses many challenges. A video stream captured by…

Computer Vision and Pattern Recognition · Computer Science 2023-11-14 Piotr Wzorek , Tomasz Kryjak

Fast neuromorphic event-based vision sensors (Dynamic Vision Sensor, DVS) can be combined with slower conventional frame-based sensors to enable higher-quality inter-frame interpolation than traditional methods relying on fixed motion…

Computer Vision and Pattern Recognition · Computer Science 2021-12-20 Adam Radomski , Andreas Georgiou , Thomas Debrunner , Chenghan Li , Luca Longinotti , Minwon Seo , Moosung Kwak , Chang-Woo Shin , Paul K. J. Park , Hyunsurk Eric Ryu , Kynan Eng

Event cameras provide asynchronous, data-driven measurements of local temporal contrast over a large dynamic range with extremely high temporal resolution. Conventional cameras capture low-frequency reference intensity information. These…

Computer Vision and Pattern Recognition · Computer Science 2018-11-02 Cedric Scheerlinck , Nick Barnes , Robert Mahony

Event cameras provide a number of benefits over traditional cameras, such as the ability to track incredibly fast motions, high dynamic range, and low power consumption. However, their application into computer vision problems, many of…

Computer Vision and Pattern Recognition · Computer Science 2019-12-20 Alex Zihao Zhu , Ziyun Wang , Kaung Khant , Kostas Daniilidis

Event cameras sense intensity changes and have many advantages over conventional cameras. To take advantage of event cameras, some methods have been proposed to reconstruct intensity images from event streams. However, the outputs are still…

Computer Vision and Pattern Recognition · Computer Science 2020-03-18 Lin Wang , Tae-Kyun Kim , Kuk-Jin Yoon

Event cameras are bio-inspired sensors that mimic the human retina by responding to brightness changes in the scene. They generate asynchronous spike-based outputs at microsecond resolution, providing advantages over traditional cameras…

Computer Vision and Pattern Recognition · Computer Science 2022-10-18 Suman Ghosh , Guillermo Gallego

Reconstruction of high-quality HDR images is at the core of modern computational photography. Significant progress has been made with multi-frame HDR reconstruction methods, producing high-resolution, rich and accurate color reconstructions…

Computer Vision and Pattern Recognition · Computer Science 2022-03-29 Richard Shaw , Sibi Catley-Chandar , Ales Leonardis , Eduardo Perez-Pellitero

Event cameras provide microsecond latency, making them suitable for 6D object pose tracking in fast, dynamic scenes where conventional RGB and depth pipelines suffer from motion blur and large pixel displacements. We introduce EventTrack6D,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-07 Jae-Young Kang , Hoonhee Cho , Taeyeop Lee , Minjun Kang , Bowen Wen , Youngho Kim , Kuk-Jin Yoon

We present ContinuityCam, a novel approach to generate a continuous video from a single static RGB image and an event camera stream. Conventional cameras struggle with high-speed motion capture due to bandwidth and dynamic range…

Computer Vision and Pattern Recognition · Computer Science 2025-04-25 Ziyun Wang , Friedhelm Hamann , Kenneth Chaney , Wen Jiang , Guillermo Gallego , Kostas Daniilidis

Event cameras are paradigm-shifting novel sensors that report asynchronous, per-pixel brightness changes called 'events' with unparalleled low latency. This makes them ideal for high speed, high dynamic range scenes where conventional…

Computer Vision and Pattern Recognition · Computer Science 2020-08-25 Timo Stoffregen , Cedric Scheerlinck , Davide Scaramuzza , Tom Drummond , Nick Barnes , Lindsay Kleeman , Robert Mahony

Event-based cameras can measure intensity changes (called `{\it events}') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the active pixel sensor (APS), the event camera allows simultaneous output…

Computer Vision and Pattern Recognition · Computer Science 2018-11-28 Liyuan Pan , Cedric Scheerlinck , Xin Yu , Richard Hartley , Miaomiao Liu , Yuchao Dai

This paper addresses the novel challenge of ``rewinding'' time from a single captured image to recover the fleeting moments missed just before the shutter button is pressed. This problem poses a significant challenge in computer vision and…

Computer Vision and Pattern Recognition · Computer Science 2024-03-21 Jingxi Chen , Brandon Y. Feng , Haoming Cai , Mingyang Xie , Christopher Metzler , Cornelia Fermuller , Yiannis Aloimonos

Non-uniform image deblurring is a challenging task due to the lack of temporal and textural information in the blurry image itself. Complementary information from auxiliary sensors such event sensors are being explored to address these…

Computer Vision and Pattern Recognition · Computer Science 2022-08-25 Patricia Vitoria , Stamatios Georgoulis , Stepan Tulyakov , Alfredo Bochicchio , Julius Erbach , Yuanyou Li

Volumetric reconstruction of dynamic scenes is an important problem in computer vision. It is especially challenging in poor lighting and with fast motion. This is partly due to limitations of RGB cameras: To capture frames under low…

Computer Vision and Pattern Recognition · Computer Science 2025-07-18 Viktor Rudnev , Gereon Fox , Mohamed Elgharib , Christian Theobalt , Vladislav Golyanik

Event cameras offer a considerable alternative to RGB cameras in many scenarios. While there are recent works on event-based novel-view synthesis, dense 3D mesh reconstruction remains scarcely explored and existing event-based techniques…

Computer Vision and Pattern Recognition · Computer Science 2026-02-04 Shreyas Sachan , Viktor Rudnev , Mohamed Elgharib , Christian Theobalt , Vladislav Golyanik

Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes. However, developing algorithms for event measurements requires a new class…

Computer Vision and Pattern Recognition · Computer Science 2018-08-14 Alex Zihao Zhu , Liangzhe Yuan , Kenneth Chaney , Kostas Daniilidis

Detecting and magnifying imperceptible high-frequency motions in real-world scenarios has substantial implications for industrial and medical applications. These motions are characterized by small amplitudes and high frequencies.…

Computer Vision and Pattern Recognition · Computer Science 2024-07-24 Yutian Chen , Shi Guo , Fangzheng Yu , Feng Zhang , Jinwei Gu , Tianfan Xue

3D hand pose estimation from monocular videos is a long-standing and challenging problem, which is now seeing a strong upturn. In this work, we address it for the first time using a single event camera, i.e., an asynchronous vision sensor…

Computer Vision and Pattern Recognition · Computer Science 2021-10-12 Viktor Rudnev , Vladislav Golyanik , Jiayi Wang , Hans-Peter Seidel , Franziska Mueller , Mohamed Elgharib , Christian Theobalt

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