Deep feature extraction constrained by local score detection
Aiming at the problem that the number and localization accuracy of feature points extracted from end-to-end feature extraction networks are not satisfactory for structure from motion(SFM),an end-to-end feature matching method that combines deformable convolution and local score detection was proposed according to the"one map,two uses"idea of feature maps in this paper.First,in feature extraction,a lightweight network with additional deformable convolution layers was used to extract multi-scale feature maps,and the feature detection maps were generated by fusing feature maps at each scale.Second,in keypoint detection and description,the channel maximum constraint was no longer considered,and the feature score map was only calculated based on local scores to avoid the influence of the numerical distribution of descriptors on feature points.Third,based on the Euclidean distance criterion and ratio test and cross-check strategies,initial feature matching was obtained,which was then optimized using the epipolar constraints.Finally,tests were conducted by using close-range and unmanned arial vehicle(UAV)images,and the results showed that our method could increase the number of feature matches and resumed 3D points with the increasing ratio within the ranges of 22.2％to 41.7％and 11.4％to 37.7％,respectively.In addition,the reprojection error of SFM was better than 1.3 pixels.
deep featuresfeature detectionmotion structure recoveryUAV image
NETL
NSTL
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