用特征集合实现毫秒级相机定位，地图构建快10倍以上
A Scene is Worth a Thousand Features: Feed-Forward Camera Localization from a Collection of Image Features
- 将多张图像特征锚定在3D空间，构建轻量地图
- 单次前向传播完成定位，最快仅需数秒
- 适用于未知场景，尤其擅长大型户外环境
视觉定位即估计图像的相机位姿，依赖于作为视觉地图的场景表示。我们选择的表示方式直接影响系统的实用性。即使从已知相机位姿的图像开始，现有最先进方法在最坏情况下仍需数小时的建图时间，最佳情况也需数分钟。本文提出FastForward，可在极短时间内构建地图并实现查询图像的即时重定位，仅需一次前向传播。核心思想是将多张建图图像表示为锚定在3D空间的特征集合，利用这些特征预测查询图像与场景的对应关系，进而估计其相机位姿。结合图像检索后，FastForward在定位精度上达到当前最优，且建图时间极短。此外，该方法对未见场景具有强泛化能力，尤其在大规模室外环境中表现优异。
原文摘要 · Abstract (English)
Visually localizing an image, i.e., estimating its camera pose, requires building a scene representation that serves as a visual map. The representation we choose has direct consequences towards the practicability of our system. Even when starting from mapping images with known camera poses, state-of-the-art approaches still require hours of mapping time in the worst case, and several minutes in the best. This work raises the question whether we can achieve competitive accuracy much faster. We introduce FastForward, a method that creates a map representation and relocalizes a query image on-the-fly in a single feed-forward pass. At the core, we represent multiple mapping images as a collection of features anchored in 3D space. FastForward utilizes these mapping features to predict image-to-scene correspondences for the query image, enabling the estimation of its camera pose. We couple FastForward with image retrieval and achieve state-of-the-art accuracy when compared to other approaches with minimal map preparation time. Furthermore, FastForward demonstrates robust generalization to unseen domains, including challenging large-scale outdoor environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。