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.

翻译：经典的基于结构的视觉定位方法虽具有高精度，但面临存储、速度和隐私性方面的权衡。近期提出的关键点场景坐标回归（KSCR）方法D2S通过利用图注意力网络增强关键点关系，并借助简单多层感知器（MLP）预测其三维坐标，有效解决了上述问题。随后通过PnP+RANSAC算法基于已建立的2D-3D对应关系确定相机位姿。尽管KSCR在多个基准测试中取得了与HLoc等最先进图像检索方法相媲美的竞争性结果，但由于深度学习模型对大规模数据的依赖，其在样本有限场景下的性能受到限制。本文针对这一挑战提出解决方案，引入了基于神经辐射场（NeRF）的关键点描述子合成流水线。通过生成新颖位姿并将其输入预训练的NeRF模型创建新视角，本方法增强了KSCR在数据稀缺环境中的泛化能力。所提系统可显著提升定位精度达50%，且数据合成仅需极少时间。此外，其模块化设计支持集成多个NeRF，为视觉定位提供了灵活高效的解决方案。相关实现已开源：https://github.com/ais-lab/DescriptorSynthesis4Feat2Map。