论文标题
SUPERGF：统一视觉本地化的本地和全局功能
SuperGF: Unifying Local and Global Features for Visual Localization
论文作者
论文摘要
先进的视觉定位技术包括图像检索挑战和6个自由度（DOF）摄像头姿势估计，例如分层定位。因此，他们必须从输入图像中提取全球和本地特征。以前的方法已经通过资源密集型或精确的减少手段（例如组合管道或多任务蒸馏）实现了这一目标。在这项研究中，我们提出了一种名为SuperGF的新颖方法，该方法有效地统一了视觉定位的本地和全局特征，从而导致本地化准确性和计算效率之间的权衡较高。具体而言，SuperGF是一个基于变压器的聚合模型，它直接在特定于图像匹配的本地特征上运行，并生成用于检索的全局功能。我们就准确性和效率进行了对方法的实验评估，证明了其优势比其他方法。我们还使用各种类型的本地功能（包括密集和稀疏的学习或手工制作的描述符）提供SUPERF的实现。
Advanced visual localization techniques encompass image retrieval challenges and 6 Degree-of-Freedom (DoF) camera pose estimation, such as hierarchical localization. Thus, they must extract global and local features from input images. Previous methods have achieved this through resource-intensive or accuracy-reducing means, such as combinatorial pipelines or multi-task distillation. In this study, we present a novel method called SuperGF, which effectively unifies local and global features for visual localization, leading to a higher trade-off between localization accuracy and computational efficiency. Specifically, SuperGF is a transformer-based aggregation model that operates directly on image-matching-specific local features and generates global features for retrieval. We conduct experimental evaluations of our method in terms of both accuracy and efficiency, demonstrating its advantages over other methods. We also provide implementations of SuperGF using various types of local features, including dense and sparse learning-based or hand-crafted descriptors.
