Representing 3D sparse map points and lines for camera relocalization
Bach-Thuan Bui Huy-Hoang Bui Dinh-Tuan Tran Joo-Ho Lee
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:
https://thpjp.github.io/pl2map/