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：我们提出一种新颖的神经体积姿态特征，称为PoseMap，旨在通过封装图像与关联相机姿态之间的信息来增强相机定位。我们的框架利用绝对姿态回归（APR）架构，并结合增强型NeRF模块。这种集成不仅有助于生成新视角以丰富训练数据集，还能学习有效的姿态特征。此外，我们将架构扩展为自监督在线对齐，使得方法能够在统一框架内对未标记图像进行使用和微调。实验表明，我们的方法在室内和室外基准场景中平均性能提升14.28%和20.51%，以最新精度超越了现有APR方法。