Comparisons between feature extraction and matching algorithms. Learning-based feature extraction and matching algorithms like SuperPoint and SuperGlue outperform traditional methods such as SIFT and NN.

  • Both qualitative and quantitative experiments demonstrate the superior performance of learning-based methods, such as SuperPoint and SuperGlue (SP&SG), compared to traditional approaches like SIFT, adalma, and NN.
  • Besides the number of keypoints and matches, SP&SG is faster than SIFT&NN.

Future Work

We plan to leverage the new end-to-end deep-learning based method VGGSfM which is fully differentiable to get subpixel accuracy and precise intrinsics on the Multiface dataset.