Multi-view data registration is an important step in the process of large objects three-dimensional (3D) measurement. But the available unmarked 3D surface auto-registration methods can result in unstable registration results when measuring objects with different surface feathers. Aiming to solve this problem, an adaptive 3D autoregistration algorithm is presented based on both geometric and photometric features. In this algorithm. a registration selection model is built to generate a registration judgment factor for synthetically evaluating the complexity of surface geometry and texture. Based on this model, an appropriate registration strategy can be adaptively selected to promise a reliable registration result. Moreover, random sample consensus（RANSAC） algorithm is used to remove the remaining wrong correspondence. The experiments use various registration results to illustrate the performance of the proposed method in different measurement applications.