To promote research on flash photography for portrait matting, this work construct the first flash/no-flash portrait matting dataset. It consists of more than 100 diverse videos captured using the green screen, in total con-taining 3,025 well-annotated alpha mattes, named Flash-No-Flash Matting Dataset.
The QRF dataset is designed to support research in quantum-native photorealistic scene rendering. It consists of high-fidelity 3D indoor and outdoor environments captured from multiple calibrated viewpoints, with detailed annotations of geometry, material properties, and lighting conditions. Each scene is processed into quantum-compatible representations for training and evaluating Quantum Radiance Fields (QRF), which leverage quantum circuits, activation functions, and quantum volume rendering.
The AMD3IR dataset is a large-scale collection of Shortwave Infrared (SWIR) and Longwave Infrared (LWIR) images, designed to advance the ongoing research in the field of drone detection and tracking. It efficiently addresses key challenges such as detecting and distinguishing small airborne objects, differentiating drones from background clutter, and overcoming visibility limitations present in conventional imaging. The dataset comprises 20,865 SWIR images with 24,994 annotated drones and 8,696 LWIR images with 10,400 annotated drones, featuring various UAV models.
This dataset comprises images of parts from real industrial scenarios and virtual reality environments. Real images are sourced from actual industrial settings, ensuring both authenticity and diversity, while virtual reality images, which make up approximately 11% of the dataset, are captured through precise 3D modeling. Approximately 30% of the part information was manually authored by industry experts, while the remaining 70% was generated by multimodal large models such as Wenxin Yiyan and GPT-4.
With multiple large open source datasets, the development of action recognition is rapid. However, we noticed the lack of annotated data of cilvil aircraft pilots, while distribution of whose action can be very different from daily casual activities. After discussion with experienced pilots and experts and close look into standard operation procedure, we present Airline-Pilot-Action (APA) benchmark, containing 5090 RGB and depth images together with corresponding flight computer data.
This dataset consists of radiation pattern images of three distinct antennas: Patch, Monopole, and Dipole, sourced from existing literature. The database was developed using pixel sampling techniques to generate a large and diverse set of images. These images were further processed to include various geometric shapes, such as symmetric and asymmetric forms, as well as triangle and square shapes, with window sizes ranging from 2 × 2 to 100 × 100 pixels.
Arc faults are a significant cause of failure in photovoltaic (PV) system and can arise due to component deterioration, installation problems, rodents chewing on wires, abrasion of insulation, or other root causes. Undetected, incipient arc faults can propagate into electrical fires. Consequently, arc-fault detectors, now mandated in many jurisdictions, are essential for safe operation of PV systems. Developing effective detectors necessitates a deep understanding of the electrical characteristics of PV arcs, which requires analyzing a large sample of arc voltage and current waveforms.
The JKU-ITS AVDM contains data from 17 participants performing different tasks with various levels of distraction.
The data collection was carried out in accordance with the relevant guidelines and regulations and informed consent was obtained from all participants.
The dataset was collected using the JKU-ITS research vehicle with automated capabilities under different illumination and weather conditions along a secure test route within the
Various modes of transportation traverse our roadways, highlighting the importance of object classification for improving traffic safety. Optical sensors that rely on visual data encounter challenges in adverse weather conditions, where poor visibility hinders target classification. In this project we use an off-the-shelf millimeter wave Frequency Modulated Continuous Wave (FMCW) radar -- Texas Instruments IWR1843BOOST module to classify on road objects. By combining the radar module, Robot Operating System (ROS), and Python scripts, we extracted a dataset of 3D point cloud images.
