JPEG Pleno Light Field Encoder With Breakpoint Dependent Affine Wavelet Transform For Disparity Maps

The dominant approach for learning keypoint detectors relies on the covariance constraint. However, existing learned detectors sometimes extract unstable keypoints from edges. To solve this problem, we propose a novel method that exploits local saliency knowledge to train a keypoint detector, and obtain a keypoint detector, called as SRK-Net, which can extract stable and repeatable keypoints. Firstly, given an image, we propose a General Local Saliency Measure method (GLSM) to assess the local saliency value for each pixel and generate a local saliency map for this image. Then we propose a Local Salient Structure Maintaining loss (LSSM) and a two-stage progressive training manner tailored for leveraging the supervision of the covariance constraint and the local saliency maps provided by our GLSM. Experimental results show that the proposed SRK-Net performs better than all the existing keypoint detectors on HPatches dataset.