Standard DatasetsShadow Segmentation Removal Dataset
Captured images from diverse indoor and outdoor settings, focusing on lighting conditions that create visible shadows and reflections. The data includes RAW-format and PNG images, shot with high-quality cameras and tripods to ensure no movement between the two shots.
Pixel-level annotation focusing on shadow and reflection removal from scenes. This dataset includes images where the primary object has been removed, and its associated shadow, reflection, or smoke effects are captured separately. The annotation ensures high precision in labeling the areas affected by these visual effects.
Sample
Specification
Dataset ID
MD-Image-104
Dataset Name
Shadow Segmentation Removal Dataset
Data Type
ImageVolume
About 200K
Data Collection
Captured images from diverse indoor and outdoor settings, focusing on lighting conditions that create visible shadows and reflections. The data includes RAW-format and PNG images, shot with high-quality cameras and tripods to ensure no movement between the two shots.
Annotation
Segmentation
Annotation Notes
● The dataset features scenes where 1-3 objects have been removed, with associated effects like shadows, reflections, and smoke. ● Two images are provided for each scene: one with the object in place and another after the object has been removed. ● The annotation labels the effects of the removed object—such as shadows, reflections, or smoke—ensuring accuracy in pixel-level segmentation. ● Strict guidelines are followed to ensure no changes in scene lighting, angle, or other contextual elements between the two images.
Application Scenarios
Fashion;Auto Driving;Ar/Vr;E-Commerce;Fashion & Apparel;Visual Entertainment
Data Collections
Captured images from diverse indoor and outdoor settings, focusing on lighting conditions that create visible shadows and reflections. The data includes RAW-format and PNG images, shot with high-quality cameras and tripods to ensure no movement between the two shots.
Data Applitcation
● Visual Effects (VFX): Ideal for enhancing shadow realism and post-production refinement.
● Autonomous Driving: Reducing misclassifications by correctly segmenting shadows and reflections that might appear as obstacles.
● Product Photography: Assisting in product photography by removing unwanted reflections or shadows that can affect the quality and perception of product images.
● AI/ML Models for Shadow Segmentation: This dataset helps train models to identify and remove shadows or reflections accurately, improving AI's performance in visual perception tasks.
Why choose our Shadow Segmentation Removal Dataset
Our clients approved the value of this dataset:
1. Autonomous Driving: Reduced false positive rates by 30% by removing tree shadows, ensuring better model performance under low-light conditions.
2. Luxury E-Commerce: Decreased product return rates by 20% by removing unwanted reflections in jewelry photos, improving product presentation.
3. Film & VFX: Shortened post-production cycles by 15 days per project by accurately removing shadows and reflections, enhancing visual continuity.
Quality Assurance
Relevant Open Datasets
To supplement our Face Parsing Dataset, users can explore these open datasets for additional resources:
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简介
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