Primitive Shape Abstraction

# Dataset: RGB-D Images for Real-World and Synthetic Object Scenes This dataset consists of both real-world and synthetic RGB-D images, designed for object detection, classification, and segmentation tasks, particularly for primitive shape recognition. ## Real-World Data - Objects: 50 distinct objects captured using a Kinect camera. - Scenes: The dataset includes both single objects and piles of objects stacked over each other. - Images: Approximately 300 RGB-D images have been collected. - Data Format: RGB images paired with corresponding depth images. ## Synthetic Data - Simulator: The synthetic dataset was automatically generated using the CoppeliaSim simulator. - Objects: Only primitive shapes, including cuboid, semisphere, sphere, cylinder, stick, ring, and cone. - Scenes: Objects are dropped over each other to form complex scenes. - Images: The dataset contains 10,000 unique RGB-D images generated by the simulator. ## Labels Both the real-world and synthetic datasets are labeled with geometric primitive shape classes and boundaries, including: - Cuboid - Semisphere - Sphere - Cylinder - Stick - Ring - Cone This dataset is designed to support research in grasp detection, object recognition, and scene understanding using RGB-D data.