Kinect and RGBD Images: Challenges and Applications

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RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, background): “Compared to a 2D intensity image, a range image is robust to the variations in color, illumination, rotation angle and scale [17].”
MobileRGBD, an open benchmark corpus for mobile RGB-D related algorithms (2014, background): “Nowadays, many researches on this topic are done using Kinect of other RGB-D sensors [1].”
Structure-Preserving Binary Representations for RGB-D Action Recognition (2016, background): “They have been widely applied to many areas such as: human activity recognition [2], robot path planning [3], object detection [4], scene labeling [5], interactive gaming [6], and 3D mapping [7].”
Learning Discriminative Representations from RGB-D Video Data (2013, methodology): “An intuitive fusion scheme was used for the Kinect gaming system [Cruz et al., 2012], in which different algorithmic modules are selected to extract the meaningful information from the RGB and depth channels, respectively.”
Deep Multimodal Data Fusion (2024, background): “In general, multimodal data refer to the data collected from different sensors, e.g., CT images, MRI images, and blood test data for the cancer diagnosis, RGB data and LiDAR data for autonomous driving system, RGB data and infrared data for skeleton detection of Kinect [28].”