Abstract:
We propose a one-step Local Feature Extraction Network framework to solve the sparse feature matching problem. In our network, we use raw camera data and the Structure fr...Show MoreMetadata
Abstract:
We propose a one-step Local Feature Extraction Network framework to solve the sparse feature matching problem. In our network, we use raw camera data and the Structure from Motion (SfM) algorithm to restore the corresponding relationships of the different feature map. Our network combines the detector and descriptor as one step to build an end-to-end Local Feature Extraction network. At the same time, the whole process is differentiable and we train our network by the loss of feature map. Finally, we train our network on indoor datasets and prove its accuracy and rapidity advantage over other methods.
Date of Conference: 30 October 2020 - 02 November 2020
Date Added to IEEE Xplore: 04 November 2020
ISBN Information: