4 citations · 4 across the 7 of their papers we have counts for
9 papers
EventHub: Data Factory for Generalizable Event-Based Stereo Networks without Active Sensors
Luca Bartolomei, Fabio Tosi, Matteo Poggi +2
We propose EventHub, a novel framework for training deep-event stereo networks without ground truth annotations from costly active sensors, relying instead on standard color images…
Interp3R: Continuous-time 3D Geometry Estimation with Frames and Events
Shuang Guo, Filbert Febryanto, Lei Sun +2
In recent years, 3D visual foundation models, pioneered by pointmap-based approaches such as DUSt3R, have attracted a lot of interest, achieving impressive accuracy and strong gene…
Geometric-Photometric Event-based 3D Gaussian Ray Tracing
Kai Kohyama, Yoshimitsu Aoki, Guillermo Gallego +1
Event cameras offer a high temporal resolution over traditional frame-based cameras, which makes them suitable for motion and structure estimation. However, it has been unclear how…
SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop
Friedhelm Hamann, Emil Mededovic, Fabian Gülhan +21
We present an overview of the Spatio-temporal Instance Segmentation (SIS) challenge held in conjunction with the CVPR 2025 Event-based Vision Workshop. The task is to predict accur…
Iterative Event-based Motion Segmentation by Variational Contrast Maximization
Ryo Yamaki, Shintaro Shiba, Guillermo Gallego +1
Event cameras provide rich signals that are suitable for motion estimation since they respond to changes in the scene. As any visual changes in the scene produce event data, it is…
Simultaneous Motion And Noise Estimation with Event Cameras
Shintaro Shiba, Yoshimitsu Aoki, Guillermo Gallego
Event cameras are emerging vision sensors whose noise is challenging to characterize. Existing denoising methods for event cameras are often designed in isolation and thus consider…
Unsupervised Joint Learning of Optical Flow and Intensity with Event Cameras
Shuang Guo, Friedhelm Hamann, Guillermo Gallego
Event cameras rely on motion to obtain information about scene appearance. This means that appearance and motion are inherently linked: either both are present and recorded in the…
ETAP: Event-based Tracking of Any Point
Friedhelm Hamann, Daniel Gehrig, Filbert Febryanto +2
Tracking any point (TAP) recently shifted the motion estimation paradigm from focusing on individual salient points with local templates to tracking arbitrary points with global im…
MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Mice
Friedhelm Hamann, Hanxiong Li, Paul Mieske +2
Enabled by large annotated datasets, tracking and segmentation of objects in videos has made remarkable progress in recent years. Despite these advancements, algorithms still strug…