Leveraging Neural Radiance Field in Descriptor Synthesis for Keypoints Scene Coordinate Regression (2403.10297v2)

Abstract: Classical structural-based visual localization methods offer high accuracy but face trade-offs in terms of storage, speed, and privacy. A recent innovation, keypoint scene coordinate regression (KSCR) named D2S addresses these issues by leveraging graph attention networks to enhance keypoint relationships and predict their 3D coordinates using a simple multilayer perceptron (MLP). Camera pose is then determined via PnP+RANSAC, using established 2D-3D correspondences. While KSCR achieves competitive results, rivaling state-of-the-art image-retrieval methods like HLoc across multiple benchmarks, its performance is hindered when data samples are limited due to the deep learning model's reliance on extensive data. This paper proposes a solution to this challenge by introducing a pipeline for keypoint descriptor synthesis using Neural Radiance Field (NeRF). By generating novel poses and feeding them into a trained NeRF model to create new views, our approach enhances the KSCR's generalization capabilities in data-scarce environments. The proposed system could significantly improve localization accuracy by up to 50% and cost only a fraction of time for data synthesis. Furthermore, its modular design allows for the integration of multiple NeRFs, offering a versatile and efficient solution for visual localization. The implementation is publicly available at: https://github.com/ais-lab/DescriptorSynthesis4Feat2Map.

• The paper presents a novel pipeline that uses Neural Radiance Fields for synthesizing keypoint descriptors in data-sparse scenarios.
• The approach integrates NeRF with scene coordinate regression techniques, demonstrating up to a 50% improvement in localization accuracy.
• Experiments on 7Scenes and 12Scenes validate the method's performance and potential for scalable visual localization.

Leveraging Neural Radiance Field in Descriptor Synthesis for Keypoints Scene Coordinate Regression

Introduction

Visual localization plays a pivotal role in numerous fields such as robotics, augmented reality, and computer vision. It entails determining the camera's position and orientation relative to a scene from a given image. Classical methods, despite their accuracy, grapple with challenges related to scalability, privacy, and the need for substantial storage. On the other hand, learning-based methods and scene coordinate regression (SCR) offer promising solutions by alleviating some of these issues. The D2S method integrates a Graph Neural Network (GNN) with a Multilayer Perceptron (MLP) for this purpose but struggles with performance under limited data. This paper introduces a novel pipeline utilizing Neural Radiance Fields (NeRF) for keypoint descriptor synthesis to enhance D2S's performance in data-deficient scenarios. The approach demonstrates notable improvements in localization accuracy, exhibiting up to a 50% accuracy increase under constrained data conditions.

Related Works

The groundwork in visual localization ranges from classical structure-based methods to learning-based and SCR strategies. Classical approaches are noted for their accuracy but fall short regarding storage and privacy concerns. Learning-based methods, though efficient in storage and privacy, often lag in accuracy. SCR approaches aim to combine the strengths of both worlds by accurately predicting 3D coordinates before camera pose estimation. The D2S method represents a step forward in SCR by leveraging keypoint relationships through a GNN and an MLP for pose estimation. However, its dependence on large data volumes for effective training remains a hindrance.

Methodology

The newly proposed descriptor synthesis pipeline integrates seamlessly with D2S and employs NeRF for generating novel views from sparse data sets. The method comprises of several steps:

1. NeRF Training: An advanced NeRF model, Nerfacto, learns the implicit scene representation from a limited dataset, synthesizing high-quality images conditioned by camera poses.
2. Camera Pose Synthesis: Utilizing spherical linear interpolation on camera poses derived from the training dataset, the pipeline creates novel camera poses to facilitate view generation.
3. Novel View Synthesis: Novel views are synthesized using the trained NeRF model, conditioned on the newly generated camera poses.
4. Descriptor Matching and Joint Training: Features are extracted from synthesized views and matched with existing ones to enrich the training dataset for KSCR, improving D2S's pose estimation capabilities.

Experiments

The experiments conducted on the 7Scenes and 12Scenes datasets demonstrate significant improvements when utilizing the proposed pipeline. By employing the Nerfacto model for rapid scene understanding and LightGlue for efficient feature matching, the method not only mitigates the data scarcity issue but also boosts the performance of D2S in visual localization tasks. The approach's efficacy is highlighted by its superior performance against existing SCR methods and even learning-based approaches in scenarios with limited training data.

Conclusions

The integration of NeRF in keypoint descriptor synthesis presents a substantial advancement in enhancing the performance of SCR methods like D2S, particularly in data-sparse scenarios. This pipeline not only buttresses the generalization abilities of D2S but also opens paths for further exploration into efficient and scalable visual localization techniques suitable for real-world applications. As research in Neural Rendering continues to evolve, there's ample opportunity for optimizing and extending this pipeline to overcome current limitations, particularly in dynamic and large-scale environments.