Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera

By Hanme Kim, Stefan Leutenegger, Andrew J. Davison. European Conference on Computer Vision, 2016

473 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/30f46fdfe1fdab60bdecaa27aaa94526dfd87ac1

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In Electrical and Electronic Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars (2018, background): “Indeed, event cameras present many advantages in all tasks related to motion estimation [2, 3, 4, 5, 6].”
Deep Learning With Spiking Neurons: Opportunities and Challenges (2018, background): “…et al., 2018) and robotics (Mueggler et al., 2017) have been released, and together with convincing results on problems where conventional systems struggle (Kim et al., 2016; Vidal et al., 2018), we expect that this will lead to increasing demand for eﬃcient event-based post-processing systems.”
HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification (2018, methodology): “Event-based Features and Object Classification Simultaneous Localization and Mapping use-cases [25, 54] drove the majority of prior work on event-based features [11, 43] for stable detection and tracking.”
Event-Based Moving Object Detection and Tracking (2018, unknown): “[9].”
Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera (2018, background): “Although several works try to explore the advantages of the high temporal resolution provided by event cameras [41, 13, 26, 43, 42, 8, 15, 30], how to make the best use of the event camera has not yet been fully investigated.”
The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception (2018, background): “The authors in [21] use events to estimate angular velocity of a camera, while [22] and [23] perform visual odometry by building an up to a scale map.”
Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics (2020, background): “(C1) What low-level video features to use? The computer vision (CV) community [20, 24, 44, 46, 59, 60, 66, 68, 83, 84] has discovered a slew of low-level video features that extract frame differences [21], such as Edge and Pixel.”
Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion (2018, background): “[9] demonstrate that a Kalman filter can reconstruct the pose of the camera and a local map.”
The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM (2016, background): “…including ours have demonstrated the use of event-based sensors in a variety of tasks, such as SLAM in 2D (Weikersdorfer et al., 2013) and 3D (Kim et al., 2016; Kueng et al., 2016; Rebecq et al., 2016b), optical flow (Bardow et al., 2016; Benosman et al., 2014; Cook et al., 2011), visual…”
Event-Based Vision: A Survey (2019, methodology): “They are also proﬁtable for depth estimation [18], [19], structured light 3D scanning [20], optical ﬂow estimation [21], [22], HDR image reconstruction [8], [23], [24] and Simultaneous Localization and Mapping (SLAM) [25], [26], [27].”
Events-To-Video: Bringing Modern Computer Vision to Event Cameras (2019, methodology): “Such specialized algorithms have demonstrated impressive performance in applications ranging from low-level vision tasks, such as visual odometry [18, 39, 53, 38, 41], feature tracking [20, 52, 15] and optical flow [5, 3, 47, 55, 46], to high-level tasks such as object classification [35, 21, 45] an”
End-to-End Learning of Representations for Asynchronous Event-Based Data (2019, methodology): “Despite their differences, both paradigms have been successfully applied on various vision tasks, including tracking [19,21,40,42], depth estimation [3,52,67], visual odometry [27, 54, 57, 66], recognition [29, 44], and optical ﬂow estimation [7, 69].”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, unknown): “high-speed motion [90] and high-dynamic range [132], [207], where standard cameras fail.”
High Speed and High Dynamic Range Video with an Event Camera (2019, methodology): “of algorithms have been specifically tailored to leverage event data, either processing the event stream in an eventby-event fashion [7], [8], [9], [10], [11], [12], or by building”
A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation (2018, background): “…two broad categories can be distinguished: 1) methods that operate on an event-by-event basis , where the state of the system (the estimated un-knowns) can change upon the arrival of a single event [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]; and 2) methods that operate on groups of events .”