arXiv Computer Vision
Event Cameras versus Frame Cameras for Synthetic-Aperture Imaging: A Simulation Study
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Hugging Face Trending Papers
Oct 14
Event Cameras versus Frame Cameras for Synthetic-Aperture Imaging: A Simulation Study
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 Computer Vision
Sep 23
High-speed Imaging through Turbulence with Event-based Light Fields
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...
arXiv Computer Vision
Oct 28
4D Reconstruction with Events & RGB
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 AI
Oct 31
Event-SAMA: Language-Grounded Video Object Segmentation on Neuromorphic Event Streams
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 Computer Vision
Oct 15
Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation
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...
arXiv Computer Vision
Sep 28
DAPEVO: Deep Adaptive Patch Frame-Event Visual Odometry
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.