UnCommon Objects in 3D

The uCO3D dataset, developed by Meta AI and showcased at CVPR 2025, represents a groundbreaking advancement in real-world 3D object data collection, addressing the longstanding challenge of balancing scale and quality in 3D vision research. The dataset comprises 170,000 meticulously captured objects across over 1,000 categories, employing the LVIS taxonomy to ensure a comprehensive reflection of real-world diversity. Distinct from its predecessors, uCO3D incorporates technical innovations like VGGSfM for accurate camera parameters and cutting-edge segmentation pipelines to resolve issues such as flickering masks. Its 3D Gaussian Splat reconstructions allow for photorealistic novel-view synthesis, setting new standards for 3D data quality. The dataset’s structured organization facilitates straightforward processing, with the integration of FiftyOne enabling enhanced interaction with the data. This combination of quality and accessibility makes uCO3D a valuable resource for advancing applications in augmented reality, robotics, and e-commerce, underscoring its potential to redefine possibilities in 3D computer vision.