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.

本文对图像三维重建中的局部特征进行了全面比较评估，包括使用强大的机器学习技术和精心设计的手工特征。在定量和定性实验中，实验结果表明，对于只有少量图像的场景，二进制特征具有很好的重建效果，并且处理速度比浮点型特征快得多。但是，对于大规模图像集，浮点型特征比二进制特征具有明显的优势。

基于图像的三维重建中局部特征的性能评估