Computer Vision and Machine Learning with RGB-D Sensors

Part I: Surveys

3D Depth Cameras in Vision: Benefits and Limitations of the Hardware
Achuta Kadambi, Ayush Bhandari and Ramesh Raskar

A State-of-the-Art Report on Multiple RGB-D Sensor Research and on Publicly Available RGB-D Datasets
Kai Berger

Part II: Reconstruction, Mapping and Synthesis

Calibration Between Depth and Color Sensors for Commodity Depth Cameras
Cha Zhang and Zhengyou Zhang

Depth Map Denoising via CDT-Based Joint Bilateral Filter
Andreas Koschan and Mongi Abidi

Human Performance Capture Using Multiple Handheld Kinects
Yebin Liu, Genzhi Ye, Yangang Wang, Qionghai Dai and Christian Theobalt

Human Centered 3D Home Applications via Low-Cost RGBD Cameras
Zhenbao Liu, Shuhui Bu and Junwei Han

Matching of 3D Objects Based on 3D Curves
Christian Feinen, Joanna Czajkowska, Marcin Grzegorzek and Longin Jan Latecki

Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinects
Kai Berger, Marc Kastner, Yannic Schroeder and Stefan Guthe

Part III: Detection, Segmentation and Tracking

RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons
Yingli Tian

RGB-D Human Identification and Tracking in a Smart Environment
Jungong Han and Junwei Han

Part IV: Learning-Based Recognition

Feature Descriptors for Depth-Based Hand Gesture Recognition
Fabio Dominio, Giulio Marin, Mauro Piazza and Pietro Zanuttigh

Hand Parsing and Gesture Recognition with a Commodity Depth Camera
Hui Liang and Junsong Yuan

Learning Fast Hand Pose Recognition
Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar Hillel, Daniel Freedman, Simon Stachniak and Cem Keskin

Realtime Hand-Gesture Recognition Using RGB-D Sensor
Yuan Yao, Fan Zhang and Yun Fu