Learning Neural Volumetric Pose Features for Camera Localization
arxiv(2024)
摘要
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
20.51
outperforming existing APR methods with state-of-the-art accuracy.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要