无需视觉描述符，用几何结构建模实现高精度相机重定位
A2-GNN: Angle-Annular GNN for Visual Descriptor-free Camera Relocalization
- 通过环形特征提取捕捉局部几何结构
- 在多个数据集上达到领先精度且计算开销低
- 适合对隐私和存储敏感的实时定位场景
视觉定位旨在估计已知场景中的6自由度（6-DoF）相机位姿。该过程的关键步骤是建立2D查询图像与3D模型之间的像素-点对应关系。当前多数先进方法依赖大量视觉描述符来建立这些对应，面临存储、隐私及模型维护挑战。无视觉描述符的直接2D-3D关键点匹配正逐渐流行，可克服上述问题。然而，现有无描述符方法普遍存在精度低或计算量大的缺陷。为此，本文提出角环图神经网络（A2-GNN），一种高效学习鲁棒几何结构表示的简单方法，通过环形特征提取实现。具体地，该方法对邻域进行聚类，并将每组的距离信息与角度作为补充信息嵌入，以捕捉局部结构。在匹配与视觉定位数据集上的评估表明，本方法在无视觉描述符的方法中实现了最先进的精度，且计算开销低。代码将发布于 https://github.com/YejunZhang/a2-gnn。
原文摘要 · Abstract (English)
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.
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