NeRF-Loc: Visual Localization with Conditional Neural Radiance Field
This addresses the problem of robust visual localization for applications like robotics or augmented reality, though it appears incremental as it builds on existing NeRF and transformer techniques.
The paper tackles visual re-localization by proposing a method that uses a conditional neural radiance field (NeRF) for 3D scene representation and direct matching with transformers, achieving higher localization accuracy than other learning-based approaches on multiple benchmarks.
We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer. A conditional neural radiance field(NeRF) is chosen as the 3D scene representation in our pipeline, which supports continuous 3D descriptors generation and neural rendering. By unifying the feature matching and the scene coordinate regression to the same framework, our model learns both generalizable knowledge and scene prior respectively during two training stages. Furthermore, to improve the localization robustness when domain gap exists between training and testing phases, we propose an appearance adaptation layer to explicitly align styles between the 3D model and the query image. Experiments show that our method achieves higher localization accuracy than other learning-based approaches on multiple benchmarks. Code is available at \url{https://github.com/JenningsL/nerf-loc}.