The DIMO dataset is a diverse set of industrial metal objects. These objects are symmetric, textureless and highly reflective, leading to challenging conditions not captured in existing datasets. Our 6D object pose estimation dataset contains both real-world and synthetic images. Real-world data is obtained by recording multi-view images of scenes with varying object shapes, materials, carriers, compositions and lighting conditions. Our dataset contains 31,200 images of 600 real-world scenes and 553,800 images of 42,600 synthetic scenes, stored in a uniļ¬ed format. The close correspondence between synthetic and real-world data, and controlled variations, will facilitate sim-to-real research. Our dataset's size and challenging nature will facilitate research on various computer vision tasks involving reflective materials. The full dataset and labeling tool is available on this GitHub repository.
