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: this https URL

通过使用神经辐射场（NeRF）合成关键点描述符的流程，提出了一种针对数据稀缺环境的解决方案，以增强关键点场景坐标回归（KSCR）模型的泛化能力，从而显著提高定位准确性