Über den Autor
Dr. Andrea Fossati and Dr. Helmut Grabner are post-doctoral researchers in the Computer Vision Laboratory at ETH Zurich, Switzerland.
Dr. Juergen Gall is a Senior Researcher at the Max Planck Institute for Intelligent Systems, Tübingen, Germany.
Dr. Xiaofeng Ren is a Research Scientist at the Intel Science and Technology Center for Pervasive Computing, Intel Labs, and an Affiliate Assistant Professor at the Department of Computer Science and Engineering of the University of Washington, Seattle, WA, USA.
Dr. Kurt Konolige is a Senior Researcher at Industrial Perception Inc., Palo Alto, CA, USA.
Part I: 3D Registration and Reconstruction
3D with Kinect
Jan Smisek, Michal Jancosek, and Tomas Pajdla
Real-Time RGB-D Mapping and 3-D Modeling on the GPU using the Random Ball Cover
Sebastian Bauer, Jakob Wasza, Felix Lugauer, Dominik Neumann, and Joachim Hornegger
A Brute Force Approach to Depth Camera Odometry
Jonathan Israël, and Aurélien Plyer
Part II: Human Body Analysis
Key Developments in Human Pose Estimation for Kinect
Pushmeet Kohli, and Jamie Shotton
A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera
Andreas Baak, Meinard Müller, Gaurav Bharaj, Hans-Peter Seidel, and Christian Theobalt
Home 3D Body Scans from a Single Kinect
Alexander Weiss, David Hirshberg, and Michael J. Black
Real-Time Hand Pose Estimation using Depth Sensors
Cem Keskin, Furkan Kiraç, Yunus Emre Kara, and Lale Akarun
Part III: RGB-D Datasets
A Category-Level 3D Object Dataset: Putting the Kinect to Work
Allison Janoch, Sergey Karayev, Yangqing Jia, Jonathan T. Barron, Mario Fritz, Kate Saenko, and Trevor Darrell
RGB-D Object Recognition: Features, Algorithms, and a Large Scale Benchmark
Kevin Lai, Liefeng Bo, Xiaofeng Ren, and Dieter Fox
RGBD-HuDaAct: A Color-Depth Video Database for Human Daily Activity Recognition
Bingbing Ni, Gang Wang, and Pierre Moulin
The potential of consumer depth cameras extends well beyond entertainment and gaming, to real-world commercial applications. This authoritative text reviews the scope and impact of this rapidly growing field, describing the most promising Kinect-based research activities, discussing significant current challenges, and showcasing exciting applications. Features: presents contributions from an international selection of preeminent authorities in their fields, from both academic and corporate research; addresses the classic problem of multi-view geometry of how to correlate images from different viewpoints to simultaneously estimate camera poses and world points; examines human pose estimation using video-rate depth images for gaming, motion capture, 3D human body scans, and hand pose recognition for sign language parsing; provides a review of approaches to various recognition problems, including category and instance learning of objects, and human activity recognition; with a Foreword by Dr. Jamie Shotton.
Describes a topic of computer vision that will be key in coming years
With a broad appeal, bridging academic and corporate research
Provides publicly-available code for many of the applications described