From the 1 of 14 linked papers with an AI index.
8 citations · 8 across the 6 of their papers we have counts for
6 papers · 1 filter
Low-Latency Event-Based Object Detection with Spatially-Sparse Linear Attention
Haiqing Hao, Zhipeng Sui, Rong Zou +5
The paper introduces Spatially‑Sparse Linear Attention (SSLA) to exploit the spatial sparsity of event‑camera data, enabling efficient parallel training and low‑latency object dete…
Hybrid Event Frame Sensors: Modeling, Calibration, and Simulation
Yunfan Lu, Nico Messikommer, Xiaogang Xu +5
Hybrid event-frame sensors integrate an Event Vision Sensor (EVS) and an Active Pixel Sensor (APS) within a single chip, combining the high dynamic range and low latency of the EVS…
Data-Driven Feature Tracking for Event Cameras With and Without Frames
Nico Messikommer, Carter Fang, Mathias Gehrig +2
Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth…
Event Spectroscopy: Event-based Multispectral and Depth Sensing using Structured Light
Christian Geckeler, Niklas Neugebauer, Manasi Muglikar +2
Uncrewed aerial vehicles (UAVs) are increasingly deployed in forest environments for tasks such as environmental monitoring and search and rescue, which require safe navigation thr…
Reinforcement Learning Meets Visual Odometry
Nico Messikommer, Giovanni Cioffi, Mathias Gehrig +1
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 cho…
State Space Models for Event Cameras
Nikola ZubiÄ, Mathias Gehrig, Davide Scaramuzza
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 exh…