Consumer Depth Cameras for Computer Vision

1. Front Matter

   Pages I-XVI

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2. 3D Registration and Reconstruction

   1. Front Matter

      Pages 1-2

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   2. 3D with Kinect

      • Jan Smisek, Michal Jancosek, Tomas Pajdla

      Pages 3-25

   3. 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, Joachim Hornegger

      Pages 27-48

   4. A Brute Force Approach to Depth Camera Odometry

      • Jonathan Israël, Aurélien Plyer

      Pages 49-60

3. Human Body Analysis

   1. Front Matter

      Pages 61-62

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   2. Key Developments in Human Pose Estimation for Kinect

      • Pushmeet Kohli, Jamie Shotton

      Pages 63-70

   3. A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera

      • Andreas Baak, Meinard Müller, Gaurav Bharaj, Hans-Peter Seidel, Christian Theobalt

      Pages 71-98

   4. Home 3D Body Scans from a Single Kinect

      • Alexander Weiss, David Hirshberg, Michael J. Black

      Pages 99-117

   5. Real Time Hand Pose Estimation Using Depth Sensors

      • Cem Keskin, Furkan Kıraç, Yunus Emre Kara, Lale Akarun

      Pages 119-137

4. RGB-D Datasets

   1. Front Matter

      Pages 139-139

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   2. A Category-Level 3D Object Dataset: Putting the Kinect to Work

      • Allison Janoch, Sergey Karayev, Yangqing Jia, Jonathan T. Barron, Mario Fritz, Kate Saenko et al.

      Pages 141-165

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

      • Kevin Lai, Liefeng Bo, Xiaofeng Ren, Dieter Fox

      Pages 167-192

   4. RGBD-HuDaAct: A Color-Depth Video Database for Human Daily Activity Recognition

      • Bingbing Ni, Gang Wang, Pierre Moulin

      Pages 193-208

5. Back Matter

   Pages 209-210

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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.

• 3D Point Cloud
• Computer Vision
• Consumer Depth Cameras
• Kinect
• Pattern Recognition

From the reviews:

“Consumer Depth Cameras for Computer Vision is among the first few written from a research point of view. It is based on research published in the Workshop on Consumer Depth Cameras and includes some of the notable researchers in the field and the original team behind Kinect itself. It’s an excellent resource for researchers working in computer vision and robotic vision or for students having knowledge of vision who wish to start working with Kinect. … highly recommend this book to any researcher of Kinect.” (Owais Mehmood, IAPR Newsletter, Vol. 35 (3–2), July, 2013)

• Computer Vision Laboratory, ETH Zürich, Zürich, Switzerland

  Andrea Fossati, Helmut Grabner

• Perceiving Systems Department, Max Planck Inst. for Intelligent Systems, Tübingen, Germany

  Juergen Gall

• Intel Science and Technology Center, Seattle, USA

  Xiaofeng Ren

• Industrial Perception, Palo Alto, USA

  Kurt Konolige

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.

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