一个模型搞定多种定位任务，效果优于单一用途方案。
MegaLoc: One Retrieval to Place Them All
- 融合多种方法与数据训练统一检索模型
- 在多个视觉定位数据集上达到新最好结果
- 适合需要跨任务通用定位的科研与工程应用
从相同位置检索图像是视觉定位、地标检索、3D重建和SLAM等计算机视觉任务的重要组成部分。然而，现有方法通常仅针对特定任务设计，在需求变化或遇到分布外数据时表现不佳。本文结合多种现有方法、训练技巧和数据集，训练出一个名为MegaLoc的通用检索模型，可在多任务中表现优异。实验表明，MegaLoc在大量视觉定位数据集上达到当前最优性能；在常见地标检索数据集上也取得显著成果；并在LaMAR数据集的视觉定位任务中，仅替换检索模块即实现新基准。代码已开源。
原文摘要 · Abstract (English)
Retrieving images from the same location as a given query is an important component of multiple computer vision tasks, like Visual Place Recognition, Landmark Retrieval, Visual Localization, 3D reconstruction, and SLAM. However, existing solutions are built to specifically work for one of these tasks, and are known to fail when the requirements slightly change or when they meet out-of-distribution data. In this paper we combine a variety of existing methods, training techniques, and datasets to train a retrieval model, called MegaLoc, that is performant on multiple tasks. We find that MegaLoc (1) achieves state of the art on a large number of Visual Place Recognition datasets, (2) impressive results on common Landmark Retrieval datasets, and (3) sets a new state of the art for Visual Localization on the LaMAR datasets, where we only changed the retrieval method to the existing localization pipeline. The code for MegaLoc is available at https://github.com/gmberton/MegaLoc
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