Learning hierarchical sparse features for RGB-(D) object recognition

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Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images (2015, methodology): “2D Object Detector in RGB-D Images 2D object detection approaches for RGB-D images treat depth as extra channel(s) appended to the color images, using handcrafted features [9], sparse coding [2, 3], or recursive neural networks [23].”
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes (2017, methodology): “With the emergence of depth cameras, several methods have been proposed for recognizing texture-less objects using RGB-D data [6], [7], [8], [9].”
Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd (2015, methodology): “Feature learning has also been used for classification[27] using RNNs, and detection[3] using sparse coding, trained with holistic object images and patches, respectively.”
Inferring 3D Object Pose in RGB-D Images (2015, background): “There have been corresponding works for RGB-D images studying the problem of object detection [17, 34, 20, 21, 35, 13, 33, 19, 23, 4], semantic segmentation [3, 12, 18, 24, 31, 13, 32], and more recently instance segmentation [13, 32].”
Aligning 3D models to RGB-D images of cluttered scenes (2015, background): “There have been corresponding works for RGB-D images studying the problems of object detection [4, 13, 17, 20, 21, 22, 24, 34, 35, 36], semantic segmentation [3, 12, 13, 19, 25, 32, 33], and more recently instance segmentation [13, 33].”