1 code implementation • 18 Sep 2023 • Nico Messikommer, Yunlong Song, Davide Scaramuzza
In Reinforcement Learning, the trade-off between exploration and exploitation poses a complex challenge for achieving efficient learning from limited samples.
no code implementations • 28 Jul 2023 • Rong Zou, Manasi Muglikar, Nico Messikommer, Davide Scaramuzza
We present the first large-scale dataset consisting of synchronized images and event sequences to evaluate our approach.
1 code implementation • 12 Jun 2023 • Yifei Liu, Mathias Gehrig, Nico Messikommer, Marco Cannici, Davide Scaramuzza
In relation to the dense counterpart that utilizes all tokens, our method realizes an increase in inference speed, achieving up to 34% faster performance for the entire network and 46% for the backbone.
1 code implementation • CVPR 2023 • Nico Messikommer, Carter Fang, Mathias Gehrig, Davide Scaramuzza
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 feature tracking, even in challenging scenarios.
1 code implementation • 18 Mar 2022 • Zhaoning Sun, Nico Messikommer, Daniel Gehrig, Davide Scaramuzza
Nonetheless, semantic segmentation with event cameras is still in its infancy which is chiefly due to the lack of high-quality, labeled datasets.
Ranked #5 on Event-based Object Segmentation on MVSEC-SEG
no code implementations • 13 Mar 2022 • Nico Messikommer, Stamatios Georgoulis, Daniel Gehrig, Stepan Tulyakov, Julius Erbach, Alfredo Bochicchio, Yuanyou Li, Davide Scaramuzza
Modern high dynamic range (HDR) imaging pipelines align and fuse multiple low dynamic range (LDR) images captured at different exposure times.
1 code implementation • 6 Sep 2021 • Nico Messikommer, Daniel Gehrig, Mathias Gehrig, Davide Scaramuzza
However, event-based vision has been held back by the shortage of labeled datasets due to the novelty of event cameras.
1 code implementation • ECCV 2020 • Nico Messikommer, Daniel Gehrig, Antonio Loquercio, Davide Scaramuzza
However, these approaches discard the spatial and temporal sparsity inherent in event data at the cost of higher computational complexity and latency.