  The local feature detector and descriptor are essential in many computer
vision tasks, such as SLAM and 3D reconstruction. In this paper, we introduce
two separate CNNs, lightweight SobelNet and DesNet, to detect key points and to
compute dense local descriptors. The detector and the descriptor work in
parallel. Sobel filter provides the edge structure of the input images as the
input of CNN. The locations of key points will be obtained after exerting the
non-maximum suppression (NMS) process on the output map of the CNN. We design
Gaussian loss for the training process of SobelNet to detect corner points as
keypoints. At the same time, the input of DesNet is the original grayscale
image, and circle loss is used to train DesNet. Besides, output maps of
SobelNet are needed while training DesNet. We have evaluated our method on
several benchmarks including HPatches benchmark, ETH benchmark, and FM-Bench.
SobelNet achieves better or comparable performance with less computation
compared with SOTA methods in recent years. The inference time of an image of
640x480 is 7.59ms and 1.09ms for SobelNet and DesNet respectively on RTX 2070
SUPER.
