arXiv:2606. 26636v1 Announce Type: cross Abstract: Event cameras asynchronously report brightness changes with microsecond-level temporal resolution, but real event data remain difficult to collect at scale because specialized sensors, careful synchronization, and task-specific annotations are required.
Event cameras asynchronously report brightness changes with microsecond-level temporal resolution, but real event data remain difficult to collect at scale because specialized sensors, careful synchronization, and task-specific annotations are required. Event-camera simulation is therefore important to event-based vision tasks.
Hybrid event-frame sensors combine an Event Vision Sensor (EVS) and an Active Pixel Sensor (APS) on a single chip, offering high dynamic range, low latency, and rich spatial intensity data. The paper introduces a unified statistics-based noise model that captures photon shot noise, dark current noise, fixed-pattern noise, and quantization noise for both APS and EVS pixels, and links EVS noise to illumination and dark current. It also presents a calibration pipeline to estimate these noise parameters from real data and proposes H-ESIM, a simulator that generates realistic RAW frames and events, validated on two hybrid sensors for tasks such as video frame interpolation and deblurring.
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.
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.
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.
arXiv:2608.28429v1 Announce Type: new Abstract: Event cameras generate asynchronous, sparse data streams with microsecond temporal resolution, but in moderate-to-high motion scenes they can produce a...
Low-light image enhancement is severely ill-posed when the input frame contains missing structure, saturated noise, and weak local contrast. Event cameras provide asynchronous brightness-change observations with high temporal resolution, but prior works often treat voxel channels as an unordered or static feature stack before fusion, rather than explicitly modeling their within-window temporal evolution, weakening the temporal evidence that makes events useful.
arXiv:2507.01927v3 Announce Type: replace Abstract: While CNNs and ViTs dominate vision architectures, all-MLP models offer a structurally simpler alternative whose patch-independent processing is na...
arXiv:2609.22479v1 Announce Type: new Abstract: We address the problem of recovering high-speed videos from dynamic scenes under extreme photon sparsity. Existing methods rely on aggregating photon d...
arXiv:2607. 09114v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is critical for automated surveillance but remains fragile under challenging conditions such as illumination variations, fast motion, and complex backgrounds when relying solely on visible light videos.
arXiv:2608. 15024v1 Announce Type: cross Abstract: Current Vision-based SLAM systems fail catastrophically when motion blur corrupts the visual input, as they attempt the ill-posed inverse problem of recovering sharp content from degraded observations.
arXiv:2609.22500v1 Announce Type: new Abstract: Autonomous navigation requires precise and efficient semantic segmentation, yet existing frame-based approaches remain limited by motion blur, glare, l...