  This paper presents a new multi-view RGB-D dataset of nine kitchen scenes,
each containing several objects in realistic cluttered environments including a
subset of objects from the BigBird dataset. The viewpoints of the scenes are
densely sampled and objects in the scenes are annotated with bounding boxes and
in the 3D point cloud. Also, an approach for detection and recognition is
presented, which is comprised of two parts: i) a new multi-view 3D proposal
generation method and ii) the development of several recognition baselines
using AlexNet to score our proposals, which is trained either on crops of the
dataset or on synthetically composited training images. Finally, we compare the
performance of the object proposals and a detection baseline to the Washington
RGB-D Scenes (WRGB-D) dataset and demonstrate that our Kitchen scenes dataset
is more challenging for object detection and recognition. The dataset is
available at: http://cs.gmu.edu/~robot/gmu-kitchens.html.
