A Performance Evaluation of Local Features for Image Based 3D Reconstruction
This work provides a performance benchmark for local features in 3D reconstruction, aiding researchers and practitioners in selecting appropriate features for different scenarios, but it is incremental as it focuses on evaluation rather than introducing new methods.
This paper conducted a comparative evaluation of state-of-the-art local features for image-based 3D reconstruction, finding that binary features are competent for controlled image sequences with reduced processing time, while float type features show a clear advantage for large-scale image sets with distracting images.
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.