论文标题
带有SOBEL滤波器和基于CNN的描述符的实时CNN基于CNN的关键点检测器接受了KePoint候选者的培训
Realtime CNN-based Keypoint Detector with Sobel Filter and CNN-based Descriptor Trained with Keypoint Candidates
论文作者
论文摘要
在许多计算机视觉任务（例如SLAM和3D重建）中，本地功能检测器和描述符至关重要。在本文中，我们介绍了两个独立的CNN，即轻巧的Sobelnet和Desnet，以检测关键点并计算密集的本地描述符。检测器和描述符并联起作用。 SOBEL滤波器将输入图像的边缘结构作为CNN的输入。在CNN的输出映射上施加了非最大抑制（NMS）过程后，将获得关键点的位置。我们为Sobelnet的训练过程设计高斯损失，以将角点视为关键点。同时，DESNET的输入是原始的灰度图像，并且使用圆形损失来训练DESNET。此外，在训练DESNET训练时，还需要Sobelnet的输出图。我们已经在几个基准测试基准中评估了我们的方法，包括HPATCHES基准，ETH基准和FM Bench。近年来，与SOTA方法相比，Sobelnet的计算能力更好或可比的性能更少。 640x480的图像的推理时间分别为7.59ms和1.09ms的Sobelnet和Desnet在RTX 2070 Super上。
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.
