UW Indoor Scenes (UW-IS) Occluded Dataset

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)

遮挡型UW室内场景（UW Indoor Scenes, UW-IS）数据集采用消费级硬件（Intel RealSense D435）精心构建，旨在还原真实机器人应用场景。该数据集包含两类完全不同的室内场景：第一类为桌面放置物体的休息室场景，第二类为货架摆放物体的模拟仓库场景。针对每类场景，我们采集了36段视频对应的RGB-D图像，每段视频包含5至7个物体，拍摄距离最大约2米。这些视频涵盖两种光照条件，并针对厨具类、食品类以及工具/杂项类三类物体类别设置了三种物体间距等级：第一级间距无任何物体遮挡，第二级间距存在部分遮挡，第三级间距则为物体紧密相邻摆放。整体而言，本数据集共涵盖20个物体类别，包含8456张图像，总计标注了42902个物体实例。我们还为所有图像提供了由LabelFusion（Marion等人，2018）生成的实例分割掩码与6D位姿标注。