Neural implicit representations such as NeRF have revolutionized 3D scene
representation with photo-realistic quality. However, existing methods for
visual localization within NeRF representations suffer from inefficiency and
scalability issues, particularly in large-scale environments. This work
proposes MatLoc-NeRF, a novel matching-based localization framework using
selected NeRF features. It addresses efficiency by employing a learnable
feature selection mechanism that identifies informative NeRF features for
matching with query images. This eliminates the need for all NeRF features or
additional descriptors, leading to faster and more accurate pose estimation. To
tackle large-scale scenes, MatLoc-NeRF utilizes a pose-aware scene partitioning
strategy. It ensures that only the most relevant NeRF sub-block generates key
features for a specific pose. Additionally, scene segmentation and a place
predictor provide fast coarse initial pose estimation. Evaluations on public
large-scale datasets demonstrate that MatLoc-NeRF achieves superior efficiency
and accuracy compared to existing NeRF-based localization methods.

MatLoc-NeRF 是一种使用选定的 NeRF 特征的基于匹配的定位框架，以提高效率和准确度，通过采用可学习的特征选择机制来消除对所有 NeRF 特征或附加描述符的需求，并利用姿态感知场景分区策略和场景分割器提供快速的粗略初始姿态估计。与现有的基于 NeRF 的定位方法相比，MatLoc-NeRF 在公共大规模数据集上表现出了更高的效率和准确度。