Zobrazeno 1 - 10
of 2 006
pro vyhledávání: '"Gehrig, P"'
Autor:
Jayanth, Royina Karegoudra, Xu, Yinshuang, Wang, Ziyun, Chatzipantazis, Evangelos, Gehrig, Daniel, Daniilidis, Kostas
Neural networks are seeing rapid adoption in purely inertial odometry, where accelerometer and gyroscope measurements from commodity inertial measurement units (IMU) are used to regress displacements and associated uncertainties. They can learn infor
Externí odkaz:
http://arxiv.org/abs/2408.06321
Publikováno v:
European Conference on Computer Vision (ECCV 2024)
Visual Odometry (VO) is essential to downstream mobile robotics and augmented/virtual reality tasks. Despite recent advances, existing VO methods still rely on heuristic design choices that require several weeks of hyperparameter tuning by human expe
Externí odkaz:
http://arxiv.org/abs/2407.15626
Autor:
Pardo, Fernando De Meer, Lehmann, Claude, Gehrig, Dennis, Nagy, Andrea, Nicoli, Stefano, Misheva, Branka Hadji, Braschler, Martin, Stockinger, Kurt
In this paper, we present an end-to-end multi-source Entity Matching problem, which we call entity group matching, where the goal is to assign to the same group, records originating from multiple data sources but representing the same real-world enti
Externí odkaz:
http://arxiv.org/abs/2406.15015
Publikováno v:
A&A 687, A136 (2024)
The final stages of a protoplanetary disk are essential for our understanding of the formation and evolution of planets. Photoevaporation is an important mechanism that contributes to the dispersal of an accretion disk and has significant consequence
Externí odkaz:
http://arxiv.org/abs/2405.05816
Publikováno v:
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Event cameras respond primarily to edges--formed by strong gradients--and are thus particularly well-suited for line-based motion estimation. Recent work has shown that events generated by a single line each satisfy a polynomial constraint which desc
Externí odkaz:
http://arxiv.org/abs/2404.00842
Publikováno v:
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 2024
Today, state-of-the-art deep neural networks that process event-camera data first convert a temporal window of events into dense, grid-like input representations. As such, they exhibit poor generalizability when deployed at higher inference frequenci
Externí odkaz:
http://arxiv.org/abs/2402.15584
Object detection with event cameras benefits from the sensor's low latency and high dynamic range. However, it is costly to fully label event streams for supervised training due to their high temporal resolution. To reduce this cost, we present LEOD,
Externí odkaz:
http://arxiv.org/abs/2311.17286
Publikováno v:
IEEE/CVF International Conference on Computer Vision (ICCV), 2023
Event-based cameras are ideal for line-based motion estimation, since they predominantly respond to edges in the scene. However, accurately determining the camera displacement based on events continues to be an open problem. This is because line feat
Externí odkaz:
http://arxiv.org/abs/2309.17054
Autor:
Pellerito, Roberto, Cannici, Marco, Gehrig, Daniel, Belhadj, Joris, Dubois-Matra, Olivier, Casasco, Massimo, Scaramuzza, Davide
Visual Odometry (VO) is crucial for autonomous robotic navigation, especially in GPS-denied environments like planetary terrains. To improve robustness, recent model-based VO systems have begun combining standard and event-based cameras. While event
Externí odkaz:
http://arxiv.org/abs/2309.09947
Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, selecting the appropriate representation for the task traditionally requir
Externí odkaz:
http://arxiv.org/abs/2304.13455