We introduce a lightweight and accurate localization method that only
utilizes the geometry of 2D-3D lines. Given a pre-captured 3D map, our approach
localizes a panorama image, taking advantage of the holistic 360 view. The
system mitigates potential privacy breaches or domain discrepancies by avoiding
trained or hand-crafted visual descriptors. However, as lines alone can be
ambiguous, we express distinctive yet compact spatial contexts from
relationships between lines, namely the dominant directions of parallel lines
and the intersection between non-parallel lines. The resulting representations
are efficient in processing time and memory compared to conventional visual
descriptor-based methods. Given the groups of dominant line directions and
their intersections, we accelerate the search process to test thousands of pose
candidates in less than a millisecond without sacrificing accuracy. We
empirically show that the proposed 2D-3D matching can localize panoramas for
challenging scenes with similar structures, dramatic domain shifts or
illumination changes. Our fully geometric approach does not involve extensive
parameter tuning or neural network training, making it a practical algorithm
that can be readily deployed in the real world. Project page including the code
is available through this link: this https URL

介绍了一种利用 2D-3D 线条几何的轻量级而准确的定位方法，通过全景视图来定位，避免了训练或手工制作视觉描述符，提取线条关系中的主导方向和非平行线条的交点等以表达独特而紧凑的空间上下文，其结果表示与传统视觉描述符方法相比，处理时间和内存上更加高效；验证了该方法在具有相似结构、领域转变或光照变化的挑战性场景中的定位效果；由于是完全几何的方法，无需进行庞大的参数调整或神经网络训练，因此具有实际应用价值，可以直接部署在实际世界中。