Time Of Flight Cameras

Time-of-flight (TOF) cameras provide a depth value at each pixel, from which the 3D structure of the scene can be estimated. This new type of active sensor makes it possible to go beyond traditional 2D image processing, directly to depth-based and 3D scene processing. Many computer vision and graphics applications can benefit from TOF data, including 3D reconstruction, activity and gesture recognition, motion capture and face detection. It is already possible to use multiple TOF cameras, in order to increase the scene coverage, and to combine the depth data with images from several colour cameras. Mixed TOF and colour systems can be used for computational photography, including full 3D scene modelling, as well as for illumination and depth-of-field manipulations. This work is a technical introduction to TOF sensors, from architectural and design issues, to selected image processing and computer vision methods.

This book provides a comprehensive overview of the key technologies and applications related to new cameras that have brought 3D data acquisition to the mass market. It covers both the theoretical principles behind the acquisition devices and the practical implementation aspects of the computer vision algorithms needed for the various applications. Real data examples are used in order to show the performances of the various algorithms. The performance and limitations of the depth camera technology are explored, along with an extensive review of the most effective methods for addressing challenges in common applications. Applications covered in specific detail include scene segmentation, 3D scene reconstruction, human pose estimation and tracking and gesture recognition. This book offers students, practitioners and researchers the tools necessary to explore the potential uses of depth data in light of the expanding number of devices available for sale. It explores the impact of these devices on the rapidly growing field of depth-based computer vision.

Today the cost of solid-state two-dimensional imagers has dramatically dropped, introducing low cost systems on the market suitable for a variety of applications, including both industrial and consumer products. However, these systems can capture only a two-dimensional projection (2D), or intensity map, of the scene under observation, losing a variable of paramount importance, i.e., the arrival time of the impinging photons. Time-Of-Flight (TOF) Range-Imaging (TOF) is an emerging sensor technology able to deliver, at the same time, depth and intensity maps of the scene under observation. Featuring different sensor resolutions, RIM cameras serve a wide community with a lot of applications like monitoring, architecture, life sciences, robotics, etc. This book will bring together experts from the sensor and metrology side in order to collect the state-of-art researchers in these fields working with RIM cameras. All the aspects in the acquisition and processing chain will be addressed, from recent updates concerning the photo-detectors, to the analysis of the calibration techniques, giving also a perspective onto new applications domains.

Cameras for 3D depth imaging, using either time-of-flight (ToF) or structured light sensors, have received a lot of attention recently and have been improved considerably over the last few years. The present techniques make full-range 3D data available at video frame rates, and thus pave the way for a much broader application of 3D vision systems. A series of workshops have closely followed the developments within ToF imaging over the years. Today, depth imaging workshops can be found at every major computer vision conference. The papers presented in this volume stem from a seminar on Time-of-Flight Imaging held at Schloss Dagstuhl in October 2012. They cover all aspects of ToF depth imaging, from sensors and basic foundations, to algorithms for low level processing, to important applications that exploit depth imaging. In addition, this book contains the proceedings of a workshop on Imaging New Modalities, which was held at the German Conference on Pattern Recognition in Saarbrücken, Germany, in September 2013. A state-of-the-art report on the Kinect sensor and its applications is followed by two reports on local and global ToF motion compensation and a novel depth capture system using a plenoptic multi-lens multi-focus camera sensor.

Time-of-Flight Cameras and Microsoft KinectTM closely examines the technology and general characteristics of time-of-flight range cameras, and outlines the best methods for maximizing the data captured by these devices. This book also analyzes the calibration issues that some end-users may face when using these type of cameras for research, and suggests methods for improving the real-time 3D reconstruction of dynamic and static scenes. Time-of-Flight Cameras and Microsoft KinectTM is intended for researchers and advanced-level students as a reference guide for time-of-flight cameras. Practitioners working in a related field will also find the book valuable.

This book is a valuable resource to deeply understand the technology used in 3D cameras. In this book, the authors summarize and compare the specifications of the main 3D cameras available in the mass market. The authors present a deep metrological analysis of the main camera based on the three main technologies: Time-of-Flight, Structured-Light and Active Stereoscopy, and provide qualitative results for any user to understand the underlying technology within 3D camera, as well as practical guidance on how to get the most of them for a given application.

This book constitutes the refereed proceedings of the Dynamic 3D Imaging Workshop, Dyn3D 2009, held in Jena, Germany as an associated event of DAGM 2009, the main international conference of the "Deutsche Arbeitsgemeinschaft für Mustererkennung". The 13 revised full papers presented were carefully reviewed and selected for inclusion in the book. The papers cover a range of topics of current interest: fundamentals of ToF- sensors, algorithms and data fusion and applications of dynamic 3D scene analysis. This book is aimed at researchers interested in novel approaches in the field of real-time range imaging.

Viele Anwendungen des Maschinellen Sehens benötigen die automatische Analyse und Rekonstruktion von statischen und dynamischen Szenen. Deshalb ist die automatische Analyse von dreidimensionalen Szenen und Objekten ein Bereich der intensiv erforscht wird. Die meisten Ansätze konzentrieren sich auf die Rekonstruktion statischer Szenen, da die Rekonstruktion nicht-statischer Geometrien viel herausfordernder ist und voraussetzt, dass dreidimensionale Szeneninformation mit hoher zeitlicher Auflösung verfügbar ist. Statische Szenenanalyse wird beispielsweise in der autonomen Navigation, für die Überwachung und für die Erhaltung des Kulturerbes eingesetzt. Andererseits eröffnet die Analyse und Rekonstruktion nicht-statischer Geometrie viel mehr Möglichkeiten, nicht nur für die bereits erwähnten Anwendungen. In der Produktion von Medieninhalten für Film und Fernsehen kann die Analyse und die Aufnahme und Wiedergabe von vollständig dreidimensionalen Inhalten verwendet werden um neue Ansichten realer Szenen zu erzeugen oder echte Schauspieler durch animierte virtuelle Charaktere zu ersetzen. Die wichtigste Voraussetzung für die Analyse von dynamischen Inhalten ist die Verfügbarkeit von zuverlässigen dreidimensionalen Szeneninformationen. Um die Entfernung von Punkten in der Szene zu bestimmen wurden meistens Stereo-Verfahren eingesetzt, aber diese Verfahren benötigen viel Rechenzeit und erreichen in Echtzeit nicht die benötigte Qualität. In den letzten Jahren haben die so genannten Laufzeitkameras das Stadium der Prototypen verlassen und sind jetzt in der Lage dichte Tiefeninformationen in vernünftiger Qualität zu einem vernünftigen Preis zu liefern. Diese Arbeit untersucht die Eignung dieser Kameras für die Analyse nicht-statischer dreidimensionaler Szenen. Bevor eine Laufzeitkamera für die Analyse eingesetzt werden kann muss sie intern und extern kalibriert werden. Darüber hinaus leiden Laufzeitkameras an systematischen Fehlern bei der Entfernungsmessung, bedingt durch ihr