This paper addresses the problem of feature-based 3D reconstruction model for close-range objects. Since it is almost impossible to find pixel-to-pixel correspondences from 2D images by algorithms when the object is imaged on a close range, the selection of feature correspondences, as well as the number and distribution of them, play important roles in the reconstruction accuracy. Then, features on representative objects are analyzed and discussed. The impact of the number and distribution of feature correspondences is analyzed by reconstructing an object with standard cylinder shape by following the reconstruction model introduced in the paper. After that, three criteria are set to guide the selection of feature correspondences for more accurate 3D reconstruction. These criteria are finally applied to the human finger since it is a typical close-range object and different number and distribution of feature correspondences can be established automatically from its 2D fingerprints. The effectiveness of the setting criteria is demonstrated by comparing the accuracy of reconstructed finger shape based on different fingerprint feature correspondences with the corresponding 3D point cloud data obtained by structured light illumination (SLI) technique which is taken as a ground truth in the paper.