用神经辐射场匹配历史照片，实现跨时代精准定位。
HistReNeRF: Historic Image Relocalisation within Contemporary Neural Radiance Field Reconstructions
- 将历史照片特征与现代场景的神经辐射场射线匹配定位。
- 在三个欧洲地标上平均降低11%平移误差、16%旋转误差。
- 无需修改历史图，直接在特征空间实现跨时域适应。
将档案照片重新定位到现代场景模型中极具挑战性，因历史与现代图像在视觉外观、可见物体和空间布局上存在差异。为此，我们提出HistReNeRF框架，通过将适配后的DINOv2块特征与当代神经辐射场（NeRF）重建中采样的候选射线进行匹配，估计历史照片的6-DoF位姿。NeRF的连续表示提供可查询的场景接口，支持候选射线采样与匹配，实现在局部定位所用特征表示中的直接域自适应。我们在一个新跨时域数据集上评估该方法，该数据集包含10,545张现代街景图像和230张来自三个欧洲地标的历史照片。基于嵌入空间的域自适应在三个场景中平均使平移误差降低11%，旋转误差降低16%。结果表明，神经场景重定位为特征空间自适应提供了自然接口，在不修改查询图像的前提下有效缓解跨时域外观差异。代码与数据集见https://github.com/ARTUROLab/HistReNeRF。
原文摘要 · Abstract (English)
Relocalising archival photographs within a contemporary scene model is challenging because historic and modern views can differ in photographic appearance, visible objects, and spatial layout. Therefore, we present HistReNeRF, a framework that estimates the 6-DoF pose of a historic photograph by matching adapted DINOv2 patch features to candidate rays sampled from a contemporary Neural Radiance Field (NeRF) reconstruction. The continuous representation of a NeRF provides a queryable scene interface from which candidate rays can be sampled and matched, enabling domain adaptation between historic photography and contemporary images directly in the feature representation used for localisation. We evaluate embedding-space-based domain adaptation against pixel-space methods on a new cross-temporal dataset comprising 10,545 contemporary street-level images and 230 archival photographs from three European landmarks. Embedding-space adaptation reduces translation and rotation errors by an average of 11% and 16%, respectively, across the three scenes. These results show that neural scene relocalisation provides a natural interface for feature-space adaptation, reducing cross-temporal appearance shift without modifying the query image. Code and dataset at https://github.com/ARTUROLab/HistReNeRF.
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