Probabilistic Online Event Downsampling
Andreu Girbau-Xalabarder, Jun Nagata, Shinichi Sumiyoshi, Ricard Marsal, Shin'ichi Satoh

TL;DR
This paper introduces POLED, a probabilistic online event downsampling framework for event cameras that adaptively prioritizes important scene events, maintaining high performance across various tasks under bandwidth constraints.
Contribution
It presents a novel probabilistic, online, and scene-adaptive event importance modeling approach for event camera data, enabling zero-shot downsampling without task-specific tuning.
Findings
Outperforms fixed heuristic methods in preserving task accuracy.
Effectively maintains scene structure in downsampled event streams.
Demonstrates versatility across multiple vision tasks.
Abstract
Event cameras capture scene changes asynchronously on a per-pixel basis, enabling extremely high temporal resolution. However, this advantage comes at the cost of high bandwidth, memory, and computational demands. To address this, prior work has explored event downsampling, but most approaches rely on fixed heuristics or threshold-based strategies, limiting their adaptability. Instead, we propose a probabilistic framework, POLED, that models event importance through an event-importance probability density function (ePDF), which can be arbitrarily defined and adapted to different applications. Our approach operates in a purely online setting, estimating event importance on-the-fly from raw event streams, enabling scene-specific adaptation. Additionally, we introduce zero-shot event downsampling, where downsampled events must remain usable for models trained on the original event stream,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
