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...
arXiv:2603.14023v3 Announce Type: replace Abstract: This work introduces and demonstrates the first system capable of imaging fast-moving extended non-rigid objects through strong atmospheric turbule...
The paper presents a lightweight extension to the Monst3R 4D reconstruction model by adding an event-camera input branch while keeping the rest of the architecture unchanged. Across three benchmark datasets, this RGB‑event fusion reduces absolute relative error by an average of 3.4% compared to the RGB‑only baseline, with the largest gains occurring under high object motion where RGB‑only methods falter. The study demonstrates that incorporating event data can meaningfully improve 4D reconstruction robustness without a full architectural redesign.
arXiv:2610. 39676v1 Announce Type: cross Abstract: Event cameras offer microsecond temporal resolution, high dynamic range and no motion blur, but referring video object segmentation (RVOS) lacks event-stream datasets and models.
arXiv:2610.20571v1 Announce Type: new Abstract: Long-form agentic video generation must turn a screenplay into shots that remain spatially and temporally consistent across nonconsecutive requests. Pa...
DAPEVO is a learned visual odometry system that independently estimates image and event correspondences at shared patch locations and fuses their correlation evidence before motion refinement. It maintains image and event descriptors for each tracked patch, using a learned scalar gate to combine modality-specific correlation embeddings for each patch–frame edge, followed by a shared recurrent refinement and bundle‑adjustment update. The method supports event‑only observations and modality‑aware keyframe culling, achieving low trajectory error even when RGB frames are sparse or degraded, outperforming DPVO, RAMP‑VO, and event‑only DEVO on UZH‑FPV and TartanEvent datasets.
The paper evaluates event-triggered recording strategies for static surveillance cameras, comparing six motion-based triggers, a CNN detector, strided detection, and hybrid approaches on a benchmark of seven scenes. Detector-based triggers reduce idle frame storage to 19‑28% versus 36‑89% for motion triggers, and remain robust under camera jitter, keeping under 29% idle frames. In addition, the study shows that increasing YOLO11n input resolution improves detection accuracy more than larger models, and that longer track memory boosts tracking IDF1, though detection remains the limiting factor.
arXiv:2608.22965v1 Announce Type: new Abstract: Accurate extrinsic calibration between event-based and frame-based cameras remains a practical bottleneck for heterogeneous stereo systems. Existing ap...
Event cameras offer microsecond temporal resolution, high dynamic range and no motion blur, but referring video object segmentation (RVOS) lacks event-stream datasets and models. We introduce Event-SA...
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.
SNF-Bench is an evaluation framework for long‑horizon fixed‑camera video generation that separates static background fidelity from dynamic flow persistence and drift leakage. It reports these three factors independently, using controlled injections of translation, rotation, scale drift, and progressive freezing to validate each metric’s sensitivity. Auditing public checkpoints shows that whole‑frame motion metrics can mislead, while SNF‑Bench reveals the true trade‑offs between motion quality and background stability.
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.