Coded structured light is one of the most effective and reliable techniques for surface reconstruction. With a calibrated projector-camera stereo system, a set of illuminating patterns are projected onto the scene by projector and the image is captured by the camera. The correspondences between projector and camera frames are calculated in the decoding process, which is used for triangulation and point cloud generation. Continuous phase shifting method is a well-known fringe projection technique for 3D scanning. A set of sinusoidal patterns showing variation of intensity or color are projected onto the surface. This proposal, introduces a novel circular phase shifting method to improve the decoding accuracy, without the trade-off between the processing speed and decoding accuracy. In the proposed approach, three-step circular phase shifting and absolute patterns are used for increasing the multiplicity since the circular patterns are coded in all directions. The stereo triangulation is more stable and accurate than line-plane intersection used in single axis structured light methods by reducing incorrect decoding errors. Robust gray patterns are applied to resolve the ambiguity resolution caused by reflectance discontinuities in the phase shifting decoding process.

7. Future directions -- Bibliography -- Authors' biographies.

Three-dimensional (3D) reconstruction plays an important role in imaging research filed. Structured light technology has been widely used in 3D imaging, recognition and measurement, indicating great industrial and commercial value. In this thesis, a simple and robust technique of Moiré topography with single-image capture and incorporating digital filtering along with a four-step digitally implemented phase-shifting method is introduced for 3D surface mapping. Feature details in the order of tens to hundreds of microns can be achieved using interferometrically generated structured light to illuminate the object surface. The feasibility of this technique is verified experimentally, and applications to metallic surfaces are demonstrated. Next, a simple non-interferometric incoherent light propagation model is introduced to perform 3D profiling of transparent objects with typical thicknesses in the order of mm to cm by analyzing the distorted captured image behind the object. A two-dimensional (2D) cosine fringe is used as the incident reference image, whose periodicity is markedly altered by the shape of the object. By monitoring the local change in the period, the surface profile is simulated and optimized to achieve minimal error with experimental data to determine the final morphology. Besides, core principles of ghost imaging and optical scan holography are also discussed.

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.

Creating 3D models of objects has been studied extensively for several decades. These models have different applications in different fields. As a result, creating 3D models is an interesting area in computer vision. There are many proposed methods for extracting 3D information of 2D images. One of the most common methods for 3D reconstruction is structured light methods. Although structured light methods can get valuable results of 3D reconstruction, they have limitations. For example, the structured light methods can get dense results on static scenes or get sparse results on dynamic scenes. In static scenes, the structured light method projects several patterns, and it results in dense models. However, in dynamic scenes, the structured light method projects just one pattern since the object is moving, and it results in sparse models. The limitation of the structured light methods in dynamic scenes is the most important motivation for this thesis. In this thesis, a single-shot structured light method is developed to overcome the sparse results in dynamic scenes. In particular, the proposed method can obtain more accurate reconstruction with just one image of dynamic scenes than that of existing methods. The new method applies global and local optimizations to establish dense correspondences. The result of simulated experiments comparison with the ground truth demonstrates that the proposed method in this thesis achieves more accurate results than that of previous methods. Lastly, the technique developed in this thesis is applied to real data in order to obtain high quality 3D reconstruction results. The results of the new method are more accurate compared to previous methods.

3D Surface Reconstruction: Multi-Scale Hierarchical Approaches presents methods to model 3D objects in an incremental way so as to capture more finer details at each step. The configuration of the model parameters, the rationale and solutions are described and discussed in detail so the reader has a strong understanding of the methodology. Modeling starts from data captured by 3D digitizers and makes the process even more clear and engaging. Innovative approaches, based on two popular machine learning paradigms, namely Radial Basis Functions and the Support Vector Machines, are also introduced. These paradigms are innovatively extended to a multi-scale incremental structure, based on a hierarchical scheme. The resulting approaches allow readers to achieve high accuracy with limited computational complexity, and makes the approaches appropriate for online, real-time operation. Applications can be found in any domain in which regression is required. 3D Surface Reconstruction: Multi-Scale Hierarchical Approaches is designed as a secondary text book or reference for advanced-level students and researchers in computer science. This book also targets practitioners working in computer vision or machine learning related fields.