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Consumer Depth Cameras for Computer Vision Research Topics and Applications

:	Andrea Fossati, Juergen Gall, Helmut Grabner, Xiaofeng Ren, Kurt Konolige
:	Andrea Fossati, Juergen Gall, Helmut Grabner, Xiaofeng Ren, Kurt Konolige
:	Consumer Depth Cameras for Computer Vision Research Topics and Applications
:	Springer-Verlag
:	9781447146407
:	Advances in Computer Vision and Pattern Recognition
:	1
:	CHF 87.00
:

:	Anwendungs-Software
:	English

:	220
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

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.

Dr. Andrea Fossati andDr. 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.

	Consumer Depth Cameras for Computer Vision	3
	Foreword	5
	Working on Human Pose Estimation for Kinect	6
	Beyond Entertainment	7
	Looking to the Future	8
	Preface	9
	Contents	12
	Acronyms	14
	Part I: 3D Registration and Reconstruction	16
	Chapter 1: 3D with Kinect	18
	1.1 Introduction	18
	1.2 Kinect as a 3D Measuring Device	19
	1.2.1 IR Image	20
	1.2.2 RGB Image	21
	1.2.3 Depth Image	21
	1.2.4 Depth Resolution	21
	1.3 Kinect Geometrical Model	23
	1.3.1 Shift Between IR Image and Depth Image	24
	1.3.2 Identi?cation of the IR Projector Geometrical Center	25
	1.3.3 Identi?cation of Effective Depth Resolutions of the IR Camera and Projector Stereo Pair	26
	1.4 Kinect Calibration	29
	1.4.1 Learning Complex Residual Errors	30
	1.5 Validation	31
	1.5.1 Kinect Depth Models Evaluation on a 3D Calibration Object	34
	1.5.2 Comparison of Kinect, SLR Stereo and 3D TOF	35
	1.5.3 Combining Kinect and Structure from Motion	36
	1.6 Conclusion	39
	References	39
	Chapter 2: Real-Time RGB-D Mapping and 3-D Modeling on the GPU Using the Random Ball Cover	41
	2.1 Introduction	42
	2.2 Related Work	43
	2.3 Methods	45
	2.3.1 Data Preprocessing on the GPU	46
	Nomenclature	46
	Landmark Extraction	47
	2.3.2 Photogeometric ICP Framework	47
	2.3.3 6-D Nearest Neighbor Search Using RBC	48
	2.4 Implementation Details	50
	2.4.1 Details Regarding the ICP Framework	50
	2.4.2 RBC Construction and Queries on the GPU	51
	RBC Construction	51
	RBC Nearest Neighbor Queries	53
	2.5 Experiments and Results	53
	2.5.1 Qualitative Results	53
	2.5.2 Performance Study	56
	Preprocessing Pipeline	56
	ICP Using RBC	56
	2.5.3 Approximate RBC	57
	2.6 Discussion and Conclusions	59
	References	60
	Chapter 3: A Brute Force Approach to Depth Camera Odometry	63
	3.1 Introduction	63
	3.2 Related Work	64
	3.3 Proposed Method	65
	3.3.1 Algorithm Overview	66
	3.3.2 Practical Issues	67
	Feature Extraction	67
	Score Evaluation	67
	3.3.3 Implementation Details	67
	3.4 Experimental Results	68
	3.4.1 Qualitative Evaluation	68
	3.4.2 Precision Analysis	69
	3.4.3 Comparison with the ICP Method	72
	3.5 Conclusion and Future Work	73
	References	73
	Part II: Human Body Analysis	75
	Chapter 4: Key Developments in Human Pose Estimation for Kinect	77
	4.1 Introduction: The Challenge	77
	4.2 Body Part Classi?cation-The Natural Markers Approach	78
	4.2.1 Generating the Training Data	79
	4.2.2 Randomized Forests for Classi?cation	79
	4.3 Random Forest Regression-The Voting Approach	80
	4.4 Context-Sensitive Pose Estimation-Conditional Regression Forests	81
	4.5 One-Shot Model Fitting: The Vitruvian Manifold	82
	4.6 Directions for Future Work	83
	References	83
	Chapter 5: A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera	85
	5.1 Introduction	86
	Contributions	87
	5.2 Related Work	88
	Intensity-Image-Based Tracking	88
	Depth-Image-Based Tracking	88
	5.3 Acquisition and Data Preparation	90
	5.3.1 Depth Data	90
	5.3.2 Model of the Actor	91
	5.3.3 Pose Database	92
	5.3.4 Normalization	93
	5.4 Pose Reconstruction Framework	94
	5.4.1 Local Optimization	95
	5.4.2 Feature Computation	95
	5.4.3 Database Lookup	101
	5.4.4 Hypothesis Selection	102
	5.5 Experiments	103
	5.5.1 Feature Extraction	103
	5.5.2 Quantitative Evaluation	103
	5.5.3 Run Time	105
	5.5.4 Qualitative Evaluation	105
	5.5.5 Limitations	108
	5.6 Conclusions	109
	References	109
	Chapter 6: Home 3D Body Scans from a Single Kinect	113
	6.1 Introduction	114
	6.2 Related Work	116
	6.3 Sensor and Preprocessing	117
	Intrinsic Calibration	118
	Stereo Calibration	118
	Depth Calibration	118
	Ground Plane	118
	Segmentation	118
	6.4 Body Model and Fitting	119
	6.4.1 SCAPE Body Model	119
	6.4.2 Pose Initialization	120
	6.4.3 Depth Objective	121
	6.4.4 Silhouette Objective	121
	6.4.5 Optimization	124
	6.5 Results	124
	From Bodies to Measurements	127
	Accuracy Relative to Laser Scans	127
	Linear Measurement Accuracy	129
	6.6 Conclusions	129
	References	130
	Chapter 7: Real Time Hand Pose Estimation Using Depth Sensors	132
	7.1 Introduction	132
	7.1.1 Related Work	134
	7.1.1.1 Hand Pose Estimation	134
	7.1.1.2 Hand Shape Recognition