The paper introduces E‑RGB‑D, a real‑time event‑based perception system that combines a Digital Light Processing projector with a monochrome event camera to produce RGB‑D data. By projecting structured light and capturing asynchronous brightness changes, the system can detect color and depth for each pixel, achieving a color detection speed of 1400 fps and a depth detection rate of 4 kHz. The approach enables frameless RGB‑D sensing and delivers colorful point clouds without compromising spatial resolution.
By Seyed Ehsan Marjani Bajestani, Giovanni Beltrame
arXiv:2606. 22574v2 Announce Type: replace-cross Abstract: While synthetic data generation resolves the manual labeling bottleneck in computer vision, minimizing the syn-to-real domain gap requires optimizing rendering variables.
By Hooman Tavakoli Ghinani, Tatjana Legler, Martin Ruskowski
The paper introduces 3L-Dataset, a physically calibrated low‑light image and video dataset created with a modular, sensor‑agnostic acquisition framework. The test bench reproduces the Midavaine night‑level scale from full moon to overcast moonless night, covering visible to near‑infrared spectra across varied environments while ensuring sub‑pixel alignment across synchronized image sextuplets. The authors evaluate deep‑learning baselines on this dataset, highlighting trade‑offs between reconstruction fidelity, perceptual quality, and inference latency under severe photon starvation.
The paper introduces a new stereo VR180 dataset captured directly with Blackmagic URSA Cine Immersive cameras, comprising 1,211 samples (636 video clips and 575 stills). Each sample is provided in camera-native Blackmagic RAW, separate-eye fisheye HEVC, and half-equirectangular HEVC formats, along with factory lens calibration and conversion tools. The authors also demonstrate that native-fisheye coding requires significantly more bitrate than half-equirectangular coding for equivalent viewport quality across all tested clips.
By Linxuan Lu
arXiv:2609.01060v1 Announce Type: cross
Abstract: Compact snapshot hyperspectral cameras provide rich instantaneous spectral measurements for ground-level machine vision, but at lower spatial resolut...
By Mohamad Jouni, Aur\'elien Godet, Mauro Dalla Mura
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...
By Nico Hessenthaler, Adam T. M\"uller, Nicolaj C. Stache
arXiv:2610.20501v1 Announce Type: new
Abstract: Low-light video processing remains an underexplored problem in computer vision, primarily due to the scarcity of high-quality ground-truth data. Existi...
By Yini Li, Joanne Lin, David Blackham, David Bull, Nantheera Anantrasirichai
The paper explores a synthetic-first training approach for detecting drones in medium- and long-wave infrared imagery, combining synthetic scene generation with fine-tuning on real data. It demonstrates that synthetic data can establish initial object representations, but real infrared data is crucial to close domain gaps and improve reliability. The study finds that aligning datasets has a greater impact on performance than increasing model size, and that semantic alignment in feature space is the strongest predictor of success, with radiometric factors like entropy and dynamic range also contributing.
By Tanel Liiv, Sander Soodla, Nzamba Bignoumba, Alma M. Liezenga, Toomas Pruuden
The paper presents a foundation-guided auto‑annotation pipeline that improves standard autonomous driving object detectors in adverse weather. By benchmarking YOLOv8, Co‑DETR, and SAM3 on a custom dataset of 25 operational scenarios, the authors find SAM3 to be the most robust and use it offline to generate pseudo‑labels. Fine‑tuning YOLOv8 on these labels boosts overall mAP by 16.04% and yields significant gains in specific conditions such as Residential Direct Sunlight (32.73%) and Highway Fog (28.65%).
By Sepideh Gohari, Goodarz Mehr, Azim Eskandarian
arXiv:2512.01495v4 Announce Type: replace
Abstract: Video instance segmentation (VIS) for low-light content remains highly challenging for both humans and machines alike, due to noise, blur and other...
By Joanne Lin, Ruirui Lin, Yini Li, David Bull, Nantheera Anantrasirichai
arXiv:2605.22455v2 Announce Type: replace-cross
Abstract: Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse...
By Valeria Pais, Malena Mendilaharzu, Daniele Faccio, Luis Oala, Christoph Clausen, Bruno Sanguinetti
VesperMUSE is a multi‑modal vision transformer that enhances image semantic segmentation under varied lighting by combining physics‑informed low‑light adaptation, thermal‑guided low‑light image enhancement, and surface normal maps. The framework fuses RGB, thermal, and normal features through a modality‑agnostic self‑attention mechanism, enabling robust segmentation without architectural changes for different sensor setups. Extensive tests show that VesperMUSE recovers missing visual cues and achieves state‑of‑the‑art performance in extreme, unstructured environments.
By Raul Balmez