The paper investigates how to model dynamic system transfer functions for event‑based vision sensors by analyzing the correspondence between temporal probes and the resulting event streams in a linear shift‑invariant optical system. Using a first‑order filter pixel model and three temporal probes—a step, a linear ramp, and an exponential ramp—the authors derive the inverse chain for different event statistics and compare the results to the system’s modulation transfer function. The study finds that the step probe is highly sensitive to threshold mismatch but robust to photon shot‑noise, whereas linear and exponential probes maintain accurate signal inference even under significant mismatch, highlighting their potential for dynamic point spread function estimation in computational neuromorphic imaging.
By Nimrod Kruger, Gregory Cohen
arXiv:2610.26933v1 Announce Type: new
Abstract: An event camera emits events at a rate given by the scene motion, object texture, illumination, its own contrast thresholds, and other settings. Across...
By Kavyansh Tyagi, Andreas Ziegler
The paper investigates how to model dynamic system transfer functions for event-based vision sensors. It analyzes three temporal probes—step, linear ramp, and exponential ramp—within a linear shift‑invariant optical system to determine which probe best matches a specified modulation transfer function. The study finds that step probes are vulnerable to threshold mismatch but robust to photon shot‑noise, while ramp probes maintain accuracy even with high mismatch, and it proposes dynamic point spread functions as part of a unified forward model for computational neuromorphic imaging.
Synthetic-aperture imaging (SAI) removes occluders such as foliage by integrating many views of a scene along a camera path. It has so far been used mainly with frame cameras. We ask whether an event...
The paper introduces SCARF, a real‑time velocity‑invariant representation for event cameras that preserves raw events while handling fast motion, stationary scenes, and independently moving objects. Unlike previous methods that convert events into image‑like forms and lose temporal detail, SCARF maintains temporal information and achieves state‑of‑the‑art performance in computational efficiency and representation quality.
By Mikihiro Ikura, Luna Gava, Jiahang Wu, Chiara Bartolozzi, Arren Glover
arXiv:2610.20309v1 Announce Type: new
Abstract: Synthetic-aperture imaging (SAI) removes occluders such as foliage by integrating many views of a scene along a camera path. It has so far been used ma...
By Jakob Hinterholzer, Mohamed Youssef, Oliver Bimber
Event cameras offer microsecond temporal resolution, low latency, and high dynamic range, making them attractive for robotics. However, labeled event-camera data for a specific robot and scene is scarce and expensive to collect, which slows the development of event-based perception and control.
The paper introduces a confidence‑normalized continuous multi‑timescale representation for event cameras, using logarithmic B‑spline temporal encoding and a geometry‑aware local confidence mechanism. When paired with a fixed feed‑forward EventCenterNet detector, this representation outperforms the compact CSTR representation on the PEDRo and Gen1 datasets. Additionally, a recursive exponential‑polynomial approximation is proposed to allow efficient event‑by‑event updates while maintaining detection performance.
By Fredrik Lundell, Per-Erik Forssen, M{\aa}rten Wadenb\"ack, Astrid Lundmark
arXiv:2610.00350v1 Announce Type: new
Abstract: Spiking Neural Networks (SNNs) offer an energy-efficient approach to processing event-camera data, yet out-of-distribution (OOD) detection remains chal...
By Arul Rana, Agrim Tripathi, Shoaib Ahmed Dipu, Md. Shaown Miah, Syed Ishtiaque Ahmed, Sayeed Shafayet Chowdhury
The paper proposes a confidence‑normalized continuous multi‑timescale event representation that encodes temporal information directly into event data using logarithmic B‑spline temporal encoding and a geometry‑aware local confidence mechanism. When applied to a fixed feed‑forward EventCenterNet detector, this representation outperforms the compact CSTR representation on the PEDRo and Gen1 datasets. Additionally, a recursive exponential‑polynomial approximation is introduced to allow efficient event‑by‑event updates while largely preserving detection performance.
arXiv:2510.26614v2 Announce Type: replace
Abstract: We propose tokenization of events and present a tokenizer, Spiking Patches, specifically designed for event cameras. Given a stream of asynchronous...
By Christoffer Koo {\O}hrstr{\o}m, Ronja G\"uldenring, Lazaros Nalpantidis
The paper addresses the problem of inaccurate latency reporting in browser-based webcam gaze trackers, which often timestamp samples at emission rather than capture time. It introduces a method that recovers a per-frame capture clock using the browser’s requestVideoFrameCallback API, enabling precise pairing of source frames with inference results or providing a verifiable lower bound when the engine does not expose its pipeline. An open TypeScript implementation and benchmark harness are released, tested on WebGazer and a FaceMesh+KRR pipeline.
By Chi-Sheng Chen, Gabriel A. Brat