FlowFormer introduces a transformer architecture into optical flow estimation and achieves
state-of-the-art performance. The core component of FlowFormer is the transformer-based …

We introduce VideoFlow, a novel optical flow estimation framework for videos. In contrast to
previous methods that learn to estimate optical flow from two frames, VideoFlow concurrently …

Event cameras respond to scene dynamics and provide signals naturally suitable for motion
estimation with advantages, such as high dynamic range. The emerging field of event-based …

Local feature matching aims at establishing sparse correspondences between a pair of
images. Recently, detector-free methods present generally better performance but are not …

We tackle the problem of Persistent Independent Particles (PIPs), also called Tracking Any
Point (TAP), in videos, which specifically aims at estimating persistent long-term trajectories …

Event-based semantic segmentation (ESS) is a fundamental yet challenging task for event
camera sensing. The difficulties in interpreting and annotating event data limit its scalability …

Recent advances in event-based vision suggest that they complement traditional cameras
by providing continuous observation without frame rate limitations and high dynamic range …

Event cameras respond to temporal dynamics, hel** to resolve ambiguities in spatio-
temporal changes for optical flow estimation. However, the unique spatio-temporal event …

Event cameras have recently gained significant traction since they open up new avenues for
low-latency and low-power solutions to complex computer vision problems. To unlock these …

This paper introduces a self-supervised learning framework designed for pre-training neural
networks tailored to dense prediction tasks using event camera data. Our approach utilizes …