Nowadays, deep learning is widely used to detect interest points and extract the corresponding descriptors and achieved suitable results for many applications of computer vision, such as image matching, three-dimensional reconstruction, simultaneous localization, and mapping. We propose an approach for interest point detection and descriptor extraction using pyramid convolution and circle loss, which is named as PC-SuperPoint. We utilize pyramid convolutions in the backbone network, which includes convolution kernels of different scales for multiscale feature extraction. The following well-designed networks are able to capture the local and global information from the obtained backbone feature maps. In addition, circle loss, which enhances weight attributes for each pair of descriptors, is also applied to improve the convergence speed in the training phase. Experiments on the HPatches dataset and KITTI dataset achieve promising results, which reveal the effectiveness of the proposed method.

中文翻译：

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PC-SuperPoint：使用金字塔卷积和圆损失的兴趣点检测和描述符提取

如今，深度学习被广泛用于检测兴趣点并提取相应的描述符，并在计算机视觉的许多应用中取得了合适的结果，例如图像匹配、三维重建、同时定位和映射。我们提出了一种使用金字塔卷积和圆损失的兴趣点检测和描述符提取方法，称为 PC-SuperPoint。我们在主干网络中利用金字塔卷积，其中包括用于多尺度特征提取的不同尺度的卷积核。以下精心设计的网络能够从获得的主干特征图中捕获局部和全局信息。此外，circle loss 增强了每对描述符的权重属性，也用于提高训练阶段的收敛速度。在 HPatches 数据集和 KITTI 数据集上的实验取得了有希望的结果，这揭示了所提出方法的有效性。