RGB-D Object Dataset

• The RGB-D Object Dataset is a large dataset of 300 common household objects. The objects are organized into 51 categories arranged using WordNet hypernym-hyponym relationships (similar to ImageNet). This dataset was recorded using a Kinect style 3D camera that records synchronized and aligned 640x480 RGB and depth images at 30 Hz. Each object was placed on a turntable and video sequences were captured for one whole rotation. For each object, there are 3 video sequences, each recorded with the camera mounted at a different height so that the object is viewed from different angles with the horizon.

• Unlike many existing datasets,such as Caltech 101 and ImageNet, objects in this dataset are organized into both categories and instances. In these datasets, the class dog contains images from many different dogs and there is no way to tell whether two images contain the same dog, while in the RGB-D Object Dataset the category soda can is divided into physically unique instances like Pepsi Can and Mountain Dew Can. The dataset also provides ground truth pose information for all 300 objects.

RGB-D Scenes Dataset v.2

• The RGB-D Scenes Dataset v2 consists of 14 scenes containing furniture (chair, coffee table, sofa, table) and a subset of the objects in the RGB-D Object Dataset (bowls, caps, cereal boxes, coffee mugs, and soda cans). Each scene is a point cloud created by aligning a set of video frames using Patch Volumes Mapping*. These 3D reconstructions and ground truth object annotations are exactly those used in our ICRA 2014 paper (see README).

RGB-D Scenes Dataset

• This dataset contains 8 scenes annotated with objects that belong to the RGB-D Object Dataset. Each scene is a single video sequence consisting of multiple RGB-D frames.