Abstract
Structure from motion (SFM) is an effective approach for reconstructing large-scale 3D scene from multiple images. In this field, many local feature methods have been proposed to detect feature point and compute descriptor. For designing a robust SFM system, how to select a good feature from existing methods is an important problem. In this paper, we aim to help different users for making decision by an experimental way for large-scale 3D reconstruction where many high resolution images are captured. To this end, we make a comprehensive evaluation of several local features on the ground truth datasets. Experimental results show that SIFT and SURF have a better performance than that of some binary features such as ORB and BRISK.
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