分离点线特征学习，提升相机重定位精度
Improved 3D Point-Line Mapping Regression for Camera Relocalization
- 点与线特征独立学习，避免相互干扰
- 在多个基准数据集上显著提升定位准确率
- 适合需要高精度3D定位的机器人与AR应用
本文提出一种改进的3D点线映射回归方法，用于相机重定位。传统方法依赖特征匹配（FM）或单一网络联合编码点与线特征。前者在大规模场景中性能好但计算开销大，后者虽降低内存占用却易过拟合，因会捕捉点线间无关相关性。我们主张点与线特征应独立学习，各具专注目标以实现最优精度。为此，提出新架构，在融合前分别强化各特征表示。实验表明，该方法显著提升3D地图点线回归性能。代码将公开于：https://github.com/ais-lab/pl2map/
原文摘要 · Abstract (English)
In this paper, we present a new approach for improving 3D point and line mapping regression for camera re-localization. Previous methods typically rely on feature matching (FM) with stored descriptors or use a single network to encode both points and lines. While FM-based methods perform well in large-scale environments, they become computationally expensive with a growing number of mapping points and lines. Conversely, approaches that learn to encode mapping features within a single network reduce memory footprint but are prone to overfitting, as they may capture unnecessary correlations between points and lines. We propose that these features should be learned independently, each with a distinct focus, to achieve optimal accuracy. To this end, we introduce a new architecture that learns to prioritize each feature independently before combining them for localization. Experimental results demonstrate that our approach significantly enhances the 3D map point and line regression performance for camera re-localization. The implementation of our method will be publicly available at: https://github.com/ais-lab/pl2map/.
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