A Fast Local Descriptor for Dense Matching

Authors: Engin Tola, Vincent Lepetit, Pascal Fua


  • Stereo image
  • descriptor
  • circle
  • quantization
  • formalization
  • binary mask
  • Depth estimation

Q2: What does the descriptor contain?

It's a concatenation of vectors. The first vector is the Gaussian of the center point with a \Sigma\0, the second set of vectors are circles lying on circle R\1, the third set of vectors are circles lying of circle R\2... Each vector contains orientation maps after a Gaussian convolution.

Q3: On which datasets did the authors try the technique?

As fas as I can get, it's a custom dataset that contains the view of the same scene from many perspectives.

Q4: What's the salience criteria for keypoints?

The aim of the technique is not matching these keypoints to each other by selecting the most appropriate ones. The computation is done on all keypoints. Hence no criteria for keypoint filtering is reported.