PNeRFLoc: Visual Localization with Point-based Neural Radiance Fields
(2312.10649)Abstract
Due to the ability to synthesize high-quality novel views, Neural Radiance Fields (NeRF) have been recently exploited to improve visual localization in a known environment. However, the existing methods mostly utilize NeRFs for data augmentation to improve the regression model training, and the performance on novel viewpoints and appearances is still limited due to the lack of geometric constraints. In this paper, we propose a novel visual localization framework, \ie, PNeRFLoc, based on a unified point-based representation. On the one hand, PNeRFLoc supports the initial pose estimation by matching 2D and 3D feature points as traditional structure-based methods; on the other hand, it also enables pose refinement with novel view synthesis using rendering-based optimization. Specifically, we propose a novel feature adaption module to close the gaps between the features for visual localization and neural rendering. To improve the efficacy and efficiency of neural rendering-based optimization, we also develop an efficient rendering-based framework with a warping loss function. Furthermore, several robustness techniques are developed to handle illumination changes and dynamic objects for outdoor scenarios. Experiments demonstrate that PNeRFLoc performs the best on synthetic data when the NeRF model can be well learned and performs on par with the SOTA method on the visual localization benchmark datasets.
Overview
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PNeRFLoc is a visual localization framework using point-based Neural Radiance Fields for improved localization accuracy.
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A feature adaption module transfers scene-agnostic features to scene-specific features for better Neural Radiance Field model integration.
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The framework optimizes photometric consistency to refine camera pose with less computational overhead due to a novel warping loss function.
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PNeRFLoc demonstrates superior performance over earlier approaches, particularly with synthetic datasets containing accurate 3D NeRF models.
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Its robustness is enhanced for challenging outdoor environments through appearance embedding and segmentation masks.
Introduction to Visual Localization
Visual localization is a crucial component in fields such as robotic navigation, augmented reality, and virtual reality. It involves determining a camera's position and orientation within a known environment based on visual input from that camera. Traditional methods for visual localization rely on matching captured image features to a pre-existing 3D map of the environment. With advancements in Neural Radiance Fields (NeRF), there have been efforts to integrate them into visual localization to improve performance. However, current approaches primarily use NeRF for data augmentation in model training without effectively leveraging geometric constraints.
PNeRFLoc Framework
The presented paper introduces PNeRFLoc, a visual localization framework employing point-based NeRF, which provides a unified representation for both initial pose estimation and pose refinement. By innovatively using a feature adaption module, PNeRFLoc closes the gap between generic features used for visual localization and those specific to neural rendering. This module enables the transfer of scene-agnostic features for initial localization to scene-specific features usable in a NeRF model. This approach facilitates the refinement of camera pose through a rendering-based optimization process, which optimizes photometric consistency between rendered and actual query images.
Efficient Optimization and Robustness
In an effort to avoid the computationally heavy process of repeatedly rendering images during optimization, PNeRFLoc introduces an efficient rendering-based framework using a novel warping loss function. This method only requires rendering the reference image once for most cases and avoids complex backpropagation through the networks, significantly enhancing both accuracy and speed of optimization. To further improve robustness, especially for outdoor environments with dynamic objects and variable lighting, techniques such as appearance embedding and segmentation masks are used.
Empirical Validation
The paper reports extensive experimental evaluations demonstrating the superior performance of PNeRFLoc compared to previous state-of-the-art methods, particularly in synthetic datasets where accurate 3D NeRF models can be learned. Additionally, PNeRFLoc shows competitive results in real-world benchmark localization datasets. A set of ablation studies corroborates the efficacy of the proposed framework components, and the authors provide an open-source codebase to foster further research and development in the field.
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