Synthetic Dataset
The UQTR dataset consists of 7838 real and synthetic images of the Université du Québec à Trois-Rivières (UQTR) campus road under normal and snow conditions. The image resolution is 1280×720. It includes lane labels in .txt files, where each row stores the set of points of a lane. The points are stored as x1 y1 x2 y2, as in the tutorial by Ruijin Liu, Zejian Yuan, Tie Liu, Zhiliang Xiong: Train and Test Your Custom Data.
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This dataset, titled "Synthetic Sand Boil Dataset for Levee Monitoring: Generated Using DreamBooth Diffusion Models," provides a comprehensive collection of synthetic images designed to facilitate the study and development of semantic segmentation models for sand boil detection in levee systems. Sand boils, a critical factor in levee integrity, pose significant risks during floods, necessitating accurate and efficient monitoring solutions.
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The C3I Synthetic Face Depth Dataset consists of 3D virtual human models and 2D rendered RGB and GT depth images in zipped version into two folders for male and female.
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The C3I Synthetic Human Dataset provides 48 female and 84 male synthetic 3D humans in fbx format generated from iClone 7 Character creator “Realistic Human 100” toolkit with variations in ethnicity, gender, race, age, and clothing. For each of these, it further provides the full-body model with five different facial expressions – Neutral, Angry, Sad, Happy, and Scared. Along with the body models, it also open-sources a data generation pipeline written in python to bring those models into a 3D Computer Graphics tool called Blender.
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Overview
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