A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor

By Christian Brandli, R. Berner, Minhao Yang, Shih-Chii Liu, Tobi Delbruck. IEEE Journal of Solid-State Circuits, 2014

1,177 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3ea7120d92e18b41e4b74038806198f924169de1

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Citations in the corpus

Events-To-Video: Bringing Modern Computer Vision to Event Cameras (2019, methodology): “The camera sensor size is set to 240 × 180 pixels (to match the resolution of the DAVIS240C sensor [7] used in our evaluation).”
End-to-End Learning of Representations for Asynchronous Event-Based Data (2019, background): “Other examples might include the instantaneous event rate or image intensity provided by such sensors as the Asynchronous Time-based Image Sensor (ATIS) [10].”
Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera (2016, positive): “Recently, cameras have been developed that interleave event data with conventional intensity frames (DAVIS [3]), or per-event intensity measurement (ATIS [21]).”
High Speed and High Dynamic Range Video with an Event Camera (2019, methodology): “These sequences were recorded using a DAVIS240C sensor [25] moving in various environments.”
Simultaneous Optical Flow and Intensity Estimation from an Event Camera (2016, background): “However, in this work we focus on exclusively analysing the event stream.”
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras (2018, background): “As a result, the network can be trained using only data captured directly from an event camera that also generates frame based images, such as the Dynamic and Active-pixel Vision (DAVIS) Sensor developed by Brandli et al. [8], circumventing the need for expensive labeling of data.”
EVO: A Geometric Approach to Event-Based 6-DOF Parallel Tracking and Mapping in Real Time (2017, methodology): “EVO uses only the event stream; the frames of the DAVIS are shown in the following figures only for illustration purposes.”
Memory and Information Processing in Neuromorphic Systems (2015, background): “Two main approaches are being pursued, depending on the readout scheme of the vision sensor: a frame-based approach, which uses inputs from conventional frame-based cameras, and an event-driven one, which uses inputs from event-driven retina-like vision sensors [52], [53], [54], [55].”
A Low Power, Fully Event-Based Gesture Recognition System (2017, background): “While there is a large body of work on hand gesture recognition, including many real-time systems [26, 47, 30], and a growing number of publications on event-based sensors like the DAVIS [2], ATIS [36], and DVS [22], there is very little work in the intersection of the two.”
DSEC: A Stereo Event Camera Dataset for Driving Scenarios (2021, methodology): “This is the case for example for the older DAVIS cameras [18] but not for state-ofthe-art event cameras [19].”
Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High-Speed Scenarios (2017, background): “contains extremely fast motions and scenes with very high dynamic range, recorded with the DAVIS [30] sensor.”
Low-latency automotive vision with event cameras (2024, positive): “They offer low motion blur, a high dynamic range, spatio-temporal sparsity and a microsecond-level resolution with lower bandwidth and power usage 16,17 .”
Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars (2018, methodology): “To predict steering angles from event images we use the publicly available DAVIS Driving Dataset 2017 (DDD17) [22].”