RGB-D Object Recognition: Features, Algorithms, and a Large Scale Benchmark

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RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “This new dataset further boosts the research on applications such as category recognition, instance recognition, 3D scene labeling and object pose estimation [9, 10, 53].”
A Large Dataset of Object Scans (2016, neutral): “[4] includes 300 objects recorded with a consumer-grade RGB-D camera.”
The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research (2016, background): “for object segmentation [15] and 6D pose estimation [16] can be contributed to the ubiquity of low-cost depth sensors.”
Learning hierarchical sparse features for RGB-(D) object recognition (2014, methodology): “(Bo et al., 2011b; Lai et al., 2012b) 80.7 In this section we demonstrate the applicability of our hierarchical feature learning approach to object detection.”
Unsupervised Feature Learning for RGB-D Based Object Recognition (2012, background): “The recognition systems developed in [20, 7, 22] use a rich set of manually designed features.”
Indoor Scene Understanding with RGB-D Images: Bottom-up Segmentation, Object Detection and Semantic Segmentation (2015, background): “collect a dataset of day-to-day objects, and propose novel kernel descriptor features to recognize these objects [31,32].”