An RGB-D dataset and evaluation methodology for detection and 6D pose estimation of texture-less objects

• 30 industry-relevant objects: no discriminative color, no texture, often similar in shape, some objects are parts of others.
• Three synchronized sensors used to capture the training and test images: Primesense CARMINE 1.09 (a structured-light RGB-D sensor), Microsoft Kinect v2 (a time-of-flight RGB-D sensor), and Canon IXUS 950 IS (a high-resolution RGB camera).
• Training images (38K from each sensor) depict individual objects against a black background.
• Test images (10K from each sensor) originate from 20 test scenes. The scene complexity varies from simple scenes with several isolated objects to very challenging ones with multiple object instances and a high amount of clutter and occlusion.
• Two types of 3D models for each object: a manually created CAD model and a semi-automatically reconstructed one.
• A new evaluation methodology which deals with pose ambiguity that can be caused by object symmetries and occlusions.

Please cite the following paper if you use the dataset. This work is licensed under Creative Commons Attribution 4.0 International.