A2-GNN: Angle-Annular GNN for Visual Descriptor-free Camera Relocalization
This addresses storage, privacy, and maintenance issues in visual localization for applications like robotics and AR/VR, though it is an incremental improvement over existing descriptor-free methods.
The paper tackles the problem of visual camera relocalization by introducing A2-GNN, a descriptor-free method that learns geometric structures to establish 2D-3D correspondences, achieving state-of-the-art accuracy with low computational overhead.
Visual localization involves estimating the 6-degree-of-freedom (6-DoF) camera pose within a known scene. A critical step in this process is identifying pixel-to-point correspondences between 2D query images and 3D models. Most advanced approaches currently rely on extensive visual descriptors to establish these correspondences, facing challenges in storage, privacy issues and model maintenance. Direct 2D-3D keypoint matching without visual descriptors is becoming popular as it can overcome those challenges. However, existing descriptor-free methods suffer from low accuracy or heavy computation. Addressing this gap, this paper introduces the Angle-Annular Graph Neural Network (A2-GNN), a simple approach that efficiently learns robust geometric structural representations with annular feature extraction. Specifically, this approach clusters neighbors and embeds each group's distance information and angle as supplementary information to capture local structures. Evaluation on matching and visual localization datasets demonstrates that our approach achieves state-of-the-art accuracy with low computational overhead among visual description-free methods. Our code will be released on https://github.com/YejunZhang/a2-gnn.