Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

HAL-NeRF: High Accuracy Localization Leveraging Neural Radiance Fields

Asterios Reppas, Grigorios-Aris Cheimariotis, Panos K. Papadopoulos, Panagiotis Frasiolas, Dimitrios Zarpalas

TL;DR

HAL-NeRF tackles monocular camera relocalization by merging a CNN pose regressor with a NeRF-based refinement module. A Nerfacto NeRF is used to generate synthetic views for data augmentation and to compute photometric loss during refinement, while a Monte Carlo particle filter iteratively sharpens the pose estimate. On the 7-Scenes and Cambridge Landmarks benchmarks, HAL-NeRF achieves state-of-the-art accuracy with translations of $0.025\ \mathrm{m}$ and rotations of $0.59^{\circ}$ (7-Scenes) and $0.04\ \mathrm{m}$ and $0.58^{\circ}$ (Cambridge), at the cost of higher computation. This work demonstrates the value of integrating NeRF-based representations into APR pipelines to substantially improve monocular relocalization performance.

Abstract

Precise camera localization is a critical task in XR applications and robotics. Using only the camera captures as input to a system is an inexpensive option that enables localization in large indoor and outdoor environments, but it presents challenges in achieving high accuracy. Specifically, camera relocalization methods, such as Absolute Pose Regression (APR), can localize cameras with a median translation error of more than $0.5m$ in outdoor scenes. This paper presents HAL-NeRF, a high-accuracy localization method that combines a CNN pose regressor with a refinement module based on a Monte Carlo particle filter. The Nerfacto model, an implementation of Neural Radiance Fields (NeRFs), is used to augment the data for training the pose regressor and to measure photometric loss in the particle filter refinement module. HAL-NeRF leverages Nerfacto's ability to synthesize high-quality novel views, significantly improving the performance of the localization pipeline. HAL-NeRF achieves state-of-the-art results that are conventionally measured as the average of the median per scene errors. The translation error was $0.025m$ and the rotation error was $0.59$ degrees and 0.04m and 0.58 degrees on the 7-Scenes dataset and Cambridge Landmarks datasets respectively, with the trade-off of increased computational time. This work highlights the potential of combining APR with NeRF-based refinement techniques to advance monocular camera relocalization accuracy.

HAL-NeRF: High Accuracy Localization Leveraging Neural Radiance Fields

TL;DR

HAL-NeRF tackles monocular camera relocalization by merging a CNN pose regressor with a NeRF-based refinement module. A Nerfacto NeRF is used to generate synthetic views for data augmentation and to compute photometric loss during refinement, while a Monte Carlo particle filter iteratively sharpens the pose estimate. On the 7-Scenes and Cambridge Landmarks benchmarks, HAL-NeRF achieves state-of-the-art accuracy with translations of and rotations of (7-Scenes) and and (Cambridge), at the cost of higher computation. This work demonstrates the value of integrating NeRF-based representations into APR pipelines to substantially improve monocular relocalization performance.

Abstract

Precise camera localization is a critical task in XR applications and robotics. Using only the camera captures as input to a system is an inexpensive option that enables localization in large indoor and outdoor environments, but it presents challenges in achieving high accuracy. Specifically, camera relocalization methods, such as Absolute Pose Regression (APR), can localize cameras with a median translation error of more than in outdoor scenes. This paper presents HAL-NeRF, a high-accuracy localization method that combines a CNN pose regressor with a refinement module based on a Monte Carlo particle filter. The Nerfacto model, an implementation of Neural Radiance Fields (NeRFs), is used to augment the data for training the pose regressor and to measure photometric loss in the particle filter refinement module. HAL-NeRF leverages Nerfacto's ability to synthesize high-quality novel views, significantly improving the performance of the localization pipeline. HAL-NeRF achieves state-of-the-art results that are conventionally measured as the average of the median per scene errors. The translation error was and the rotation error was degrees and 0.04m and 0.58 degrees on the 7-Scenes dataset and Cambridge Landmarks datasets respectively, with the trade-off of increased computational time. This work highlights the potential of combining APR with NeRF-based refinement techniques to advance monocular camera relocalization accuracy.

Paper Structure

This paper contains 22 sections, 6 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Absolute Pose Regression
  4. NeRFs in Localization
  5. NeRF - Overview
  6. Method
  7. Pose Regressor
  8. NeRF Model
  9. Data Augmentation
  10. Pose Regressor Architecture
  11. Pose Optimization
  12. Prediction Step
  13. Update Step
  14. Resampling Step
  15. Implementation Details
  16. ...and 7 more sections

Figures (4)

  • Figure 1: HAL-NeRF Pipeline.
  • Figure 2: Comparison of query images and refined results for Stairs (7-Scenes dataset) and Old Hospital (Cambridge dataset).
  • Figure 3: Median translational and rotational errors for the Chess scene over 50 iterations.
  • Figure 4: Visualization of particle convergence in the rviz environment from the $1^{st}$ to the $50^{th}$ iteration step.