Purpose

This paper aims to design a novel feature descriptor to improve the performance of feature matching in challenge scenes, such as low texture and wide-baseline scenes. Common descriptors are not suitable for low texture scenes and other challenging scenes mainly owing to encoding only one kind of features. The proposed feature descriptor considers multiple features and their locations, which is more expressive.

Design/methodology/approach

A graph neural network–based descriptors enhancement algorithm for feature matching is proposed. In this paper, point and line features are the primary concerns. In the graph, commonly used descriptors for points and lines constitute the nodes and the edges are determined by the geometric relationship between points and lines. After the graph convolution designed for incomplete join graph, enhanced descriptors are obtained.

Findings

Experiments are carried out in indoor, outdoor and low texture scenes. The experiments investigate the real-time performance, rotation invariance, scale invariance, viewpoint invariance and noise sensitivity of the descriptors in three types of scenes. The results show that the enhanced descriptors are robust to scene changes and can be used in wide-baseline matching.

Originality/value

A graph structure is designed to represent multiple features in an image. In the process of building graph structure, the geometric relation between multiple features is used to establish the edges. Furthermore, a novel hybrid descriptor for points and lines is obtained using graph convolutional neural network. This enhanced descriptor has the advantages of both point features and line features in feature matching.

中文翻译：

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HDPL：基于图神经网络的点线混合描述符

目的

本文旨在设计一种新颖的特征描述符，以提高挑战场景中特征匹配的性能，例如低纹理和宽基线场景。通用描述符不适用于低纹理场景和其他具有挑战性的场景，主要是因为仅编码一种特征。提出的特征描述符考虑了多个特征及其位置，更具表现力。

设计/方法/方法

提出了一种基于图神经网络的特征匹配描述符增强算法。在本文中，点和线特征是主要关注点。在图中，常用的点和线的描述符构成节点，边由点和线之间的几何关系决定。为不完全连接图设计的图卷积后，得到增强的描述符。

发现

实验分别在室内、室外和低纹理场景中进行。实验研究了描述符在三种场景下的实时性、旋转不变性、尺度不变性、视点不变性和噪声敏感性。结果表明，增强的描述符对场景变化具有鲁棒性，可用于宽基线匹配。

原创性/价值

图结构旨在表示图像中的多个特征。在构建图结构的过程中，利用多个特征之间的几何关系来建立边。此外，使用图卷积神经网络获得了一种新颖的点和线混合描述符。这种增强描述子在特征匹配中兼具点特征和线特征的优点。