  This paper performs a comprehensive and comparative evaluation of the state
of the art local features for the task of image based 3D reconstruction. The
evaluated local features cover the recently developed ones by using powerful
machine learning techniques and the elaborately designed handcrafted features.
To obtain a comprehensive evaluation, we choose to include both float type
features and binary ones. Meanwhile, two kinds of datasets have been used in
this evaluation. One is a dataset of many different scene types with
groundtruth 3D points, containing images of different scenes captured at fixed
positions, for quantitative performance evaluation of different local features
in the controlled image capturing situations. The other dataset contains
Internet scale image sets of several landmarks with a lot of unrelated images,
which is used for qualitative performance evaluation of different local
features in the free image collection situations. Our experimental results show
that binary features are competent to reconstruct scenes from controlled image
sequences with only a fraction of processing time compared to use float type
features. However, for the case of large scale image set with many distracting
images, float type features show a clear advantage over binary ones.
