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Photometric stereo is a technique for estimating surface normals using images captured under varying illumination. However, conventional frame-based photometric stereo methods are limited in real-world applications due to their reliance on…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Hyunwoo Kim , Won-Hoe Kim , Sanghoon Lee , Jianfei Cai , Giljoo Nam , Jae-Sang Hyun

Event cameras such as DAVIS can simultaneously output high temporal resolution events and low frame-rate intensity images, which own great potential in capturing scene motion, such as optical flow estimation. Most of the existing optical…

Computer Vision and Pattern Recognition · Computer Science 2022-11-18 Zhexiong Wan , Yuchao Dai , Yuxin Mao

Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous events instead of intensity frames. Compared to conventional image sensors, they offer significant advantages: high temporal resolution,…

Computer Vision and Pattern Recognition · Computer Science 2020-10-23 Javier Hidalgo-Carrió , Daniel Gehrig , Davide Scaramuzza

Estimating continuous optical flow is a fundamental yet challenging problem in dynamic visual perception. Event-based cameras, with microsecond latency and high dynamic range, capture brightness changes asynchronously, offering a unique…

Computer Vision and Pattern Recognition · Computer Science 2026-05-27 Rui Hu , Song Wu , Wen Yang , Jinjian Wu

We present a method for estimating dense continuous-time optical flow from event data. Traditional dense optical flow methods compute the pixel displacement between two images. Due to missing information, these approaches cannot recover the…

Computer Vision and Pattern Recognition · Computer Science 2024-02-13 Mathias Gehrig , Manasi Muglikar , Davide Scaramuzza

Event cameras are novel vision sensors that report per-pixel brightness changes as a stream of asynchronous "events". They offer significant advantages compared to standard cameras due to their high temporal resolution, high dynamic range…

Computer Vision and Pattern Recognition · Computer Science 2021-02-19 Daniel Gehrig , Michelle Rüegg , Mathias Gehrig , Javier Hidalgo Carrio , Davide Scaramuzza

Eye-tracking is a vital technology for human-computer interaction, especially in wearable devices such as AR, VR, and XR. The realization of high-speed and high-precision eye-tracking using frame-based image sensors is constrained by their…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Hongwei Ren , Xiaopeng Lin , Hongxiang Huang , Yue Zhou , Bojun Cheng

Event cameras are novel bio-inspired sensors that offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.). Optical flow estimation methods that work on packets of events trade off speed for accuracy,…

Computer Vision and Pattern Recognition · Computer Science 2024-03-05 Shintaro Shiba , Yoshimitsu Aoki , Guillermo Gallego

Event-based cameras are dynamic vision sensors that provide asynchronous measurements of changes in per-pixel brightness at a microsecond level. This makes them significantly faster than conventional frame-based cameras, and an appealing…

Computer Vision and Pattern Recognition · Computer Science 2021-10-01 Sai Vemprala , Sami Mian , Ashish Kapoor

Dynamic Vision Sensors (DVS) capture event data with high temporal resolution and low power consumption, presenting a more efficient solution for visual processing in dynamic and real-time scenarios compared to conventional video capture…

Computer Vision and Pattern Recognition · Computer Science 2025-02-14 Yiting Dong , Xiang He , Guobin Shen , Dongcheng Zhao , Yang Li , Yi Zeng

We consider the problem of reconstructing a dynamic scene observed from a stereo camera. Most existing methods for depth from stereo treat different stereo frames independently, leading to temporally inconsistent depth predictions. Temporal…

Computer Vision and Pattern Recognition · Computer Science 2023-05-04 Nikita Karaev , Ignacio Rocco , Benjamin Graham , Natalia Neverova , Andrea Vedaldi , Christian Rupprecht

Event cameras, which are asynchronous bio-inspired vision sensors, have shown great potential in computer vision and artificial intelligence. However, the application of event cameras to object-level motion estimation or tracking is still…

Computer Vision and Pattern Recognition · Computer Science 2020-09-21 Haosheng Chen , David Suter , Qiangqiang Wu , Hanzi Wang

Event cameras respond to scene dynamics and offer advantages to estimate motion. Following recent image-based deep-learning achievements, optical flow estimation methods for event cameras have rushed to combine those image-based methods…

Computer Vision and Pattern Recognition · Computer Science 2024-03-05 Shintaro Shiba , Yoshimitsu Aoki , Guillermo Gallego

We present a method that leverages the complementarity of event cameras and standard cameras to track visual features with low-latency. Event cameras are novel sensors that output pixel-level brightness changes, called "events". They offer…

Computer Vision and Pattern Recognition · Computer Science 2019-01-21 Daniel Gehrig , Henri Rebecq , Guillermo Gallego , Davide Scaramuzza

Current optical flow methods exploit the stable appearance of frame (or RGB) data to establish robust correspondences across time. Event cameras, on the other hand, provide high-temporal-resolution motion cues and excel in challenging…

Computer Vision and Pattern Recognition · Computer Science 2025-08-20 Qianang Zhou , Junhui Hou , Meiyi Yang , Yongjian Deng , Youfu Li , Junlin Xiong

Event cameras are rapidly emerging as powerful vision sensors for 3D reconstruction, uniquely capable of asynchronously capturing per-pixel brightness changes. Compared to traditional frame-based cameras, event cameras produce sparse yet…

Computer Vision and Pattern Recognition · Computer Science 2025-12-23 Chuanzhi Xu , Haoxian Zhou , Langyi Chen , Haodong Chen , Zeke Zexi Hu , Zhicheng Lu , Ying Zhou , Vera Chung , Qiang Qu , Weidong Cai

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

Event-based cameras are increasingly utilized in various applications, owing to their high temporal resolution and low power consumption. However, a fundamental challenge arises when deploying multiple such cameras: they operate on…

Robotics · Computer Science 2023-10-02 Wanli Xing , Shijie Lin , Guangze Zheng , Yanjun Du , Jia Pan

Event camera is a new type of sensor that is different from traditional cameras. Each pixel is triggered asynchronously by an event. The trigger event is the change of the brightness irradiated on the pixel. If the increment or decrement is…

Computer Vision and Pattern Recognition · Computer Science 2022-03-28 Kun Xiao , Pengju Li , Guohui Wang , Zhi Li , Yi Chen , Yongfeng Xie , Yuqiang Fang

Event cameras are novel sensors that report brightness changes in the form of asynchronous "events" instead of intensity frames. They have significant advantages over conventional cameras: high temporal resolution, high dynamic range, and…

Computer Vision and Pattern Recognition · Computer Science 2019-04-18 Henri Rebecq , René Ranftl , Vladlen Koltun , Davide Scaramuzza