Starting from a collection of images the first step consists in relating the different images to each other. This is not as easy problem. As seen in the previous chapter, a restricted number of corresponding points is sufficient to determine the geometric relationship or multi-view constraints between the images. Since not all points are equally suited for matching (e.g. a pixel in a homogeneous region), the first step consist of selecting a number of interesting points or feature points. Some approaches also use other features, such as lines or curves, but these will not be discussed here. Feature points are compared and a number of potential correspondences are obtained. From these the multi-view constraints can be computed. However, since the correspondence problem is an ill-posed problem, the set of corresponding points is typically contaminated with an important number of wrong matches or outliers. In this case, a traditional least-squares approach will fail and therefore a robust method is needed. Once the multi-view constraints have been obtained they can be used to guide the search for additional correspondences. These can then be used to further refine the results for the multi-view constraints.