54 citations · 105 across the 14 of their papers we have counts for
18 papers
SEE Challenge 2026: Event-Guided Brightness Adjustment Across a Broad Illumination Range
Yunfan Lu, Mingchao Xu, Hanyu Zhou +9
Event cameras provide a high dynamic range and preserve brightness-change cues in lighting conditions where conventional RGB frames may be noisy or saturated. To benchmark event-gu…
A 25-s/inf Event-driven Graph Neural Network Processor with Spatiotemporal Caching and Spline Convolution for Ultra-low-latency AI at the Edge
Adrian Kneip, Martin Lefebvre, Daniel Gehrig +4
Dynamic-vision-sensor (DVS) cameras generate events on a per-pixel basis with a s-level temporal resolution, calling for new algorithm-hardware co-design approaches compared to…
ETHEREAL: A 25.6-s/inf. Low-latency Event-driven Graph-neural-network Processor for High-resolution Vision at the Edge
Adrian Kneip, Martin Lefebvre, Daniel Gehrig +4
Dynamic vision sensors (DVS) are enticing candidates to reach the low-latency, sub-ms target of edge-vision applications, as they generate events with a s-level time resolution.…
Low-Latency Event-Based Object Detection with Spatially-Sparse Linear Attention
Haiqing Hao, Zhipeng Sui, Rong Zou +4
Event cameras provide sequential visual data with spatial sparsity and high temporal resolution, making them attractive for low-latency object detection. Existing asynchronous even…
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…
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…
A roadmap for AI in robotics
Aude Billard, Alin Albu-Schaeffer, Michael Beetz +8
AI technologies, including deep learning, large-language models have gone from one breakthrough to the other. As a result, we are witnessing growing excitement in robotics at the p…
Regularity and Stability Properties of Selective SSMs with Discontinuous Gating
Nikola Zubić, Davide Scaramuzza
Selective State-Space Models (SSMs) such as Mamba have become central to long-sequence modeling. Still, their stability is poorly understood: their state-space coefficients are mod…
Maximizing Asynchronicity in Event-based Neural Networks
Haiqing Hao, Nikola Zubić, Weihua He +3
Event cameras deliver visual data with high temporal resolution, low latency, and minimal redundancy, yet their asynchronous, sparse sequential nature challenges standard tensor-ba…
GG-SSMs: Graph-Generating State Space Models
Nikola Zubić, Davide Scaramuzza
State Space Models (SSMs) are powerful tools for modeling sequential data in computer vision and time series analysis domains. However, traditional SSMs are limited by fixed, one-d…
S7: Selective and Simplified State Space Layers for Sequence Modeling
Taylan Soydan, Nikola Zubić, Nico Messikommer +2
A central challenge in sequence modeling is efficiently handling tasks with extended contexts. While recent state-space models (SSMs) have made significant progress in this area, t…
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…
Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory
Nikola Zubić, Federico Soldá, Aurelio Sulser +1
Despite their successes, deep learning models struggle with tasks requiring complex reasoning and function composition. We present a theoretical and empirical investigation into th…
Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging
Manasi Muglikar, Siddharth Somasundaram, Akshat Dave +3
Traditional cameras face a trade-off between low-light performance and high-speed imaging: longer exposure times to capture sufficient light results in motion blur, whereas shorter…
Hilti SLAM Challenge 2023: Benchmarking Single + Multi-session SLAM across Sensor Constellations in Construction
Ashish Devadas Nair, Julien Kindle, Plamen Levchev +1
Simultaneous Localization and Mapping systems are a key enabler for positioning in both handheld and robotic applications. The Hilti SLAM Challenges organized over the past years h…
State Space Models for Event Cameras
Nikola Zubić, 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…
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…
Dense Continuous-Time Optical Flow from Events and Frames
Mathias Gehrig, Manasi Muglikar, Davide Scaramuzza
We present a method for estimating dense continuous-time optical flow from event data. Traditional dense optical flow methods compute the pixel displacement between two images. Due…