This book provides an introduction to the foundations of three-dimensional computer vision and describes recent contributions to the field. Geometric methods include linear and bundle adjustment based approaches to scene reconstruction and camera calibration, stereo vision, point cloud segmentation, and pose estimation of rigid, articulated, and flexible objects. Photometric techniques evaluate the intensity distribution in the image to infer three-dimensional scene structure, while real-aperture approaches exploit the behavior of the point spread function. It is shown how the integration of several methods increases reconstruction accuracy and robustness. Applications scenarios include industrial quality inspection, metrology, human-robot-interaction, and remote sensing.
"I do recommend serious consideration of this book for a one-semester advanced graduate course on methods for 3D scene reconstruction, based on the first five chapters. For graduate students pursuing PhDs in the field of computer vision, this is a book they won't want to miss." (Zhaoqiang Lai, ACM Computing Reviews, Mar 8 2013, Review # CR141001)