Papers With Code 2 | ML Benchmarks, SotA Results & Code

UW Indoor Scenes (UW-IS) Occluded dataset is curated using commodity hardware (Intel RealSense D435) to reflect real world robotics scenarios. It consists of two completely different indoor environments. The first environment is a lounge where the objects are placed on a tabletop. The second environment is a mock warehouse setup where the objects are placed on a shelf. For each of these environments, we have RGB-D images from 36 videos comprising five to seven objects each, taken from distances up to approximately 2m. The videos cover two different lighting conditions, three different levels of object separation for three different object categories (i.e., kitchen objects, food items, and tools/miscellaneous). The first level of object separation is such that there is no object occlusion. The second level of object separation is such that some occlusion occurs, while the third level is where the objects are placed extremely close together. Overall, the dataset considers 20 object classes and consists of 8,456 images, which have a total of 42,902 object instances. We also provide instance segmentation masks and 6D pose annotations for all the images generated using LabelFusion (Marion et al., 2018)