Recent advancements in visual localization and mapping have demonstrated
considerable success in integrating point and line features. However, expanding
the localization framework to include additional mapping components frequently
results in increased demand for memory and computational resources dedicated to
matching tasks. In this study, we show how a lightweight neural network can
learn to represent both 3D point and line features, and exhibit leading pose
accuracy by harnessing the power of multiple learned mappings. Specifically, we
utilize a single transformer block to encode line features, effectively
transforming them into distinctive point-like descriptors. Subsequently, we
treat these point and line descriptor sets as distinct yet interconnected
feature sets. Through the integration of self- and cross-attention within
several graph layers, our method effectively refines each feature before
regressing 3D maps using two simple MLPs. In comprehensive experiments, our
indoor localization findings surpass those of Hloc and Limap across both
point-based and line-assisted configurations. Moreover, in outdoor scenarios,
our method secures a significant lead, marking the most considerable
enhancement over state-of-the-art learning-based methodologies. The source code
and demo videos of this work are publicly available at:
this https URL

通过利用一个轻量级神经网络，我们展示了如何学习表示三维点和线特征，并通过多个学习映射的力量在姿态准确性方面取得领先的结果。我们通过使用一个转换器块编码线特征，将其有效地转化为独特的点式描述符，并在几个图层中集成自注意力和交叉注意力，从而有效地优化每个特征后使用两个简单的多层感知器进行三维地图回归。在综合实验中，我们的室内定位结果在基于点和基于线的配置上都超过了 Hloc 和 Limap 的结果。而且，在户外场景中，我们的方法取得了显著的领先地位，是目前基于学习的方法中最重要的改进。