Abstract
State-of-the-art (SOTA) hierarchical localisation pipelines (HLoc) rely on image retrieval (IR) techniques to establish 2D-3D correspondences by selecting the $k$ most similar images from a reference image database for a given query image. Although higher values of $k$ enhance localisation robustness, the computational cost for feature matching increases linearly with $k$. In this paper, we observe that queries that are the most similar to images in the database result in a higher proportion of feature matches and, thus, more accurate positioning. Thus, a small number of images is sufficient for queries very similar to images in the reference database. We then propose a novel approach, AIR-HLoc, which divides query images into different localisation difficulty levels based on their similarity to the reference image database. We consider an image with high similarity to the reference image as an easy query and an image with low similarity as a hard query. Easy queries show a limited improvement in accuracy when increasing $k$. Conversely, higher values of $k$ significantly improve accuracy for hard queries. Given the limited improvement in accuracy when increasing $k$ for easy queries and the significant improvement for hard queries, we adapt the value of $k$ to the query's difficulty level. Therefore, AIR-HLoc optimizes processing time by adaptively assigning different values of $k$ based on the similarity between the query and reference images without losing accuracy. Our extensive experiments on the Cambridge Landmarks, 7Scenes, and Aachen Day-Night-v1.1 datasets demonstrate our algorithm's efficacy, reducing 30\%, 26\%, and 11\% in computational overhead while maintaining SOTA accuracy compared to HLoc with fixed image retrieval.
Abstract (translated)
先进的（SOTA）层次局部定位管道（HLoc）依赖于图像检索（IR）技术来通过从参考图像数据库中选择与给定查询图像最相似的$k$个图像来建立2D-3D对应关系。尽管$k$较高的值提高了局部定位的鲁棒性，但基于特征匹配的计算成本随$k$线性增加。在本文中，我们观察到与数据库中图像最相似的查询导致更多的特征匹配，从而实现更准确的定位。因此，对于与参考数据库中图像非常相似的查询，只需要几张图片就足够了。然后，我们提出了名为AIR-HLoc的新方法，根据查询图像与参考图像数据库的相似性将查询图像划分为不同的局部化难度级别。我们将高相似度的图像视为容易的查询，低相似度的图像视为困难的查询。容易的查询在增加$k$时，准确度改进有限。相反，高$k$值对于困难的查询有显著的提高准确度。由于在容易的查询和困难的查询之间，增加$k$对准确度的提升有限，我们将$k$的值自适应地分配给查询的难度级别。因此，AIR-HLoc通过根据查询图像和参考图像之间的相似性动态分配不同的$k$值来实现处理时间的优化，同时保持与固定图像检索的SOTA准确度。我们对剑桥地标、7Scenes和Aachen Day-Night-v1.1数据集的广泛实验证明了我们的算法的有效性，将计算开销减少30%、26%和11%，同时保持与固定图像检索的SOTA准确度。
URL
https://arxiv.org/abs/2403.18281
