We introduce a novel neural volumetric pose feature, termed PoseMap, designed to enhance camera localization by encapsulating the information between images and the associated camera poses. Our framework leverages an Absolute Pose Regression (APR) architecture, together with an augmented NeRF module. This integration not only facilitates the generation of novel views to enrich the training dataset but also enables the learning of effective pose features. Additionally, we extend our architecture for self-supervised online alignment, allowing our method to be used and fine-tuned for unlabelled images within a unified framework. Experiments demonstrate that our method achieves 14.28% and 20.51% performance gain on average in indoor and outdoor benchmark scenes, outperforming existing APR methods with state-of-the-art accuracy.

翻译：我们提出了一种新颖的神经体积位姿特征，称为PoseMap，旨在通过封装图像与相关相机位姿之间的信息来增强相机定位。我们的框架利用绝对位姿回归（APR）架构，并结合一个增强的NeRF模块。这种集成不仅促进了新视图的生成以丰富训练数据集，还使得学习有效的位姿特征成为可能。此外，我们扩展了该架构以实现自监督的在线对齐，从而允许我们的方法在统一框架内用于未标记图像并进行微调。实验表明，我们的方法在室内和室外基准场景中平均实现了14.28%和20.51%的性能提升，以最先进的精度超越了现有的APR方法。