Algorithmic evaluation demonstrates superior efficiency and accurate pose estimation across large-scale datasets, indicating scalable visual localization for implicit representations.
Key Points
MatLoc-NeRF improves visual localization efficiency by selecting informative NeRF features to match query images directly, eliminating redundant descriptors and speeding up inference.
Pose-aware scene partitioning ensures that only the relevant NeRF sub-block generates features, while scene segmentation and place prediction offer fast initial coarse pose estimation.
Evaluations on public large-scale datasets show enhanced pose estimation accuracy, supporting scalable visual localization frameworks across expansive real-world 3D environments.