Multiple Viewpoint Recognition and Localization

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RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “Compared to other Kinect object datasets, the images of B3DO are taken in “the wild” [35, 91] places by an automatic turntable setting.”
Detection-based object labeling in 3D scenes (2012, background): “However, most existing object detection systems only output bounding box detections , as in Helmer et al. [17] who used sliding window classifiers to detect objects from several viewpoints.”
3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey (2014, background): “6 UBC VRS [52] Point Cloud 603 60 o, c Available”
A category-level 3-D object dataset: Putting the Kinect to work (2011, methodology): “UBC Visual Robot Survey [3, 18]: This dataset from UBC provides training data for 4 categories (mug, bottle, bowl, and shoe) and 30 cluttered scenes for testing.”
3D Object Detection with Multiple Kinects (2012, background): “Furthermore we distinguish 9 object categories while [7,14] only distinguish 4 and 2, respectively.”