Providing a computer the capability to estimate the three-dimensional geometry of a scene is a fundamental problem in computer vision. A classical systems that has been adopted for solving this problem is the so-called stereo vision system (stereo system). Such a system is constituted by a couple of cameras and it exploits the principle of triangulation in order to provide an estimate of the framed scene. In the last ten years, new devices based on the time-of-flight principle have been proposed in order to solve the same problem, i.e., matricial Time-of-Flight range cameras (ToF cameras).
This thesis focuses on the analysis of the two systems (ToF and stereo cam- eras) from a theoretical and an experimental point of view. ToF cameras are introduced in Chapter 2 and stereo systems in Chapter 3. In particular, for the case of the ToF cameras, a new formal model that describes the acquisition process is derived and presented. In order to understand strengths and weaknesses of such different systems, a comparison methodology is introduced and explained in Chapter 4. From the analysis of ToF cameras and stereo systems it is possible to understand the complementarity of the two systems and it is intuitive to figure that a synergic fusion of their data might provide an improvement in the quality of the measurements preformed by the two devices. In Chapter 5 a method for fusing ToF and stereo data based on a probability approach is presented. In Chapter 6 a method that exploits color and three-dimensional geometry information for solving the classical problem of scene segmentation is explaine
