Point cloud construction using digital fringe projection (PCCDFP) is a noncontact technique for acquiring dense point clouds to represent the 3D shapes of objects. Most existing PCCDFP systems use projection patterns consisting of straight fringes with fixed fringe pitches. In certain situations, such patterns do not give the best results. In our earlier work, we have shown that for surfaces with large range of normal directions, patterns that use curved fringes with spatial pitch variation can significantly improve the process of constructing point clouds. This paper describes algorithms for automatically generating adaptive projection patterns that use curved fringes with spatial pitch variation to provide improved results for an object being measured. We also describe the supporting algorithms that are needed for utilizing adaptive projection patterns. Both simulation and physical experiments show that adaptive patterns are able to achieve improved performance, in terms of measurement accuracy and coverage, as compared to fixed-pitch straight fringe patterns.