ImLoc：重新审视基于图像的视觉定位
ImLoc: Revisiting Visual Localization with Image-based Representation
AI总结：
ImLoc通过结合图像与深度图提升视觉定位精度，实现高效存储与计算，达到新的最先进水平。
摘要：
现有的视觉定位方法通常要么是基于2D图像的，易于构建和维护但有效几何推理有限，要么是基于3D结构的，虽然精度高但需要集中重建且难以更新。在本工作中，我们重新审视基于图像的视觉定位，并提出在每个图像上添加估计的深度图以捕捉几何结构。通过有效使用密集匹配器，这种表示不仅易于构建和维护，而且在挑战性条件下实现了最高精度。借助紧凑压缩和GPU加速的LO-RANSAC实现，整个流程在存储和计算上都高效，并允许在精度和最高内存效率之间灵活权衡。我们的方法在各种标准基准上实现了新的最先进精度，并在可比地图大小下优于现有内存高效方法。代码将在https://github.com/cvg/Hierarchical-Localization上提供。
英文摘要：
Existing visual localization methods are typically either 2D image-based, which are easy to build and maintain but limited in effective geometric reasoning, or 3D structure-based, which achieve high accuracy but require a centralized reconstruction and are difficult to update. In this work, we revisit visual localization with a 2D image-based representation and propose to augment each image with estimated depth maps to capture the geometric structure. Supported by the effective use of dense matchers, this representation is not only easy to build and maintain, but achieves highest accuracy in challenging conditions. With compact compression and a GPU-accelerated LO-RANSAC implementation, the whole pipeline is efficient in both storage and computation and allows for a flexible trade-off between accuracy and highest memory efficiency. Our method achieves a new state-of-the-art accuracy on various standard benchmarks and outperforms existing memory-efficient methods at comparable map sizes. Code will be available at https://github.com/cvg/Hierarchical-Localization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
