用带深度图的2D图像实现高效高精度视觉定位，兼顾易维护与强几何推理。
ImLoc: Revisiting Visual Localization with Image-based Representation
- 以带深度图的2D图像表示替代传统3D结构，提升几何建模能力。
- 在多个基准测试中达到新最优精度，且内存效率优于现有方法。
- 支持快速更新和低存储开销，适合实际部署场景。
现有视觉定位方法通常分为两类：基于2D图像的方法易于构建与维护，但几何推理能力有限；基于3D结构的方法精度高，但需集中重建且难以更新。本文重新思考基于2D图像表示的视觉定位，提出为每张图像附加估计的深度图以捕捉几何结构。结合密集匹配器的有效使用，该表示不仅易于构建与维护，更在复杂条件下实现了最高精度。通过紧凑压缩与GPU加速的LO-RANSAC实现，整个流程在存储与计算上均高效，支持精度与内存效率间的灵活权衡。该方法在多个标准基准上取得新最优结果，且在相近地图规模下超越现有内存高效方法。代码将公开于https://github.com/cvg/Hierarchical-Localization。
原文摘要 · Abstract (English)
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.
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