Learning Neural Volumetric Pose Features for Camera Localization
Jingyu Lin Jiaqi Gu Bojian Wu Lubin Fan Renjie Chen Ligang Liu Jieping Ye
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.