collaborators

8 papers

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

Interp3R: Continuous-time 3D Geometry Estimation with Frames and Events

Shuang Guo, Filbert Febryanto, Lei Sun +1

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 genera…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…