DGC-GNN: Descriptor-free Geometric-Color Graph Neural Network for 2D-3D Matching
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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.
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