# NuRF: Nudging the Particle Filter in Radiance Fields for Robot Visual Localization

arXiv: https://arxiv.org/abs/2406.00312 · HTML: https://mlsanity.com/paper/2406.00312/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2024.

## Built on by (most cited first, 2 of 2)

- [Camera Pose Refinement via 3D Gaussian Splatting](https://mlsanity.com/paper/2508.17876/) (2025)
- [SG2Loc: Sequential Visual Localization on 3D Scene Graphs](https://mlsanity.com/paper/2606.11880/) (2026)

## Abstract (arXiv)

Can we localize a robot on a map only using monocular vision? This study presents NuRF, an adaptive and nudged particle filter framework in radiance fields for 6-DoF robot visual localization. NuRF leverages recent advancements in radiance fields and visual place recognition. Conventional visual place recognition meets the challenges of data sparsity and artifact-induced inaccuracies. By utilizing radiance field-generated novel views, NuRF enhances visual localization performance and combines coarse global localization with the fine-grained pose tracking of a particle filter, ensuring continuous and precise localization. Experimentally, our method converges 7 times faster than existing Monte Carlo-based methods and achieves localization accuracy within 1 meter, offering an efficient and resilient solution for indoor visual localization.