  We present a diverse dataset of industrial metal objects. These objects are
symmetric, textureless and highly reflective, leading to challenging conditions
not captured in existing datasets. Our dataset contains both real-world and
synthetic multi-view RGB images with 6D object pose labels. Real-world data is
obtained by recording multi-view images of scenes with varying object shapes,
materials, carriers, compositions and lighting conditions. This results in over
30,000 images, accurately labelled using a new public tool. Synthetic data is
obtained by carefully simulating real-world conditions and varying them in a
controlled and realistic way. This leads to over 500,000 synthetic images. 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 dataset and accompanying resources are made
available on the project website at https://pderoovere.github.io/dimo.
