RGB-D Object Dataset
A large dataset of 300 common household objects recorded using a Kinect style 3D camera.
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.
Aside from isolated views of the 300 objects, the RGB-D Object Dataset also includes 8 annotated video sequences of natural scenes containing objects from the dataset (The RGB-D Scenes Dataset). The scenes cover common indoor environments, including office workspaces, meeting rooms, and kitchen areas. The objects are visible from different viewpoints and distances and may be partially or completely occluded in some frames.
Visit the Dataset Website for more details.
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Publications
- Multipath Sparse Coding Using Hierarchical Matching Pursuit (2013)
- RGB-D Object Recognition: Features, Algorithms, and a Large Scale Benchmark (2013)
- Detection-based Object Labeling in 3D Scenes (2012)
- Unsupervised Feature Learning for RGB-D Based Object Recognition (2012)
- A Large-Scale Hierarchical Multi-View RGB-D Object Dataset (2011)
- A Scalable Tree-based Approach for Joint Object and Pose Recognition (2011)
- Depth Kernel Descriptors for Object Recognition (2011)
- Hierarchical Matching Pursuit for Recognition: Architecture and Fast Algorithms (2011)
- Object Recognition with Hierarchical Kernel Descriptors (2011)
- Sparse Distance Learning for Object Recognition Combining RGB and Depth Information (2011)

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