A systematic evaluation shows that learned descriptors, GMS pruning, and a coarse-to-fine RANSAC plus LMedS combination improve fundamental matrix estimation over the SIFT plus RANSAC baseline.
Learning local feature descriptors with triplets and shallow convolutional neural networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2019 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
An Evaluation of Feature Matchers for Fundamental Matrix Estimation
A systematic evaluation shows that learned descriptors, GMS pruning, and a coarse-to-fine RANSAC plus LMedS combination improve fundamental matrix estimation over the SIFT plus RANSAC baseline.