Direct matching of 2D keypoints in an input image to a 3D point cloud of the
scene without requiring visual descriptors has garnered increased interest due
to its lower memory requirements, inherent privacy preservation, and reduced
need for expensive 3D model maintenance compared to visual descriptor-based
methods. However, existing algorithms often compromise on performance,
resulting in a significant deterioration compared to their descriptor-based
counterparts. In this paper, we introduce DGC-GNN, a novel algorithm that
employs a global-to-local Graph Neural Network (GNN) that progressively
exploits geometric and color cues to represent keypoints, thereby improving
matching robustness. Our global-to-local procedure encodes both Euclidean and
angular relations at a coarse level, forming the geometric embedding to guide
the local point matching. We evaluate DGC-GNN on both indoor and outdoor
datasets, demonstrating that it not only doubles the accuracy of the
state-of-the-art descriptor-free algorithm but, also, substantially narrows the
performance gap between descriptor-based and descriptor-free methods. The code
and trained models will be made publicly available.

本论文介绍了一种名为 DGC-GNN 的新算法，它采用全局到局部的图神经网络来逐步利用几何和颜色线索来表示关键点，从而提高匹配的鲁棒性。DGC-GNN 不仅使无描述符算法的精度翻倍，而且还显著缩小了基于描述符和无描述符方法之间的性能差距。