  In contrast to sparse keypoints, a handful of line segments can concisely
encode the high-level scene layout, as they often delineate the main structural
elements. In addition to offering strong geometric cues, they are also
omnipresent in urban landscapes and indoor scenes. Despite their apparent
advantages, current line-based reconstruction methods are far behind their
point-based counterparts. In this paper we aim to close the gap by introducing
LIMAP, a library for 3D line mapping that robustly and efficiently creates 3D
line maps from multi-view imagery. This is achieved through revisiting the
degeneracy problem of line triangulation, carefully crafted scoring and track
building, and exploiting structural priors such as line coincidence,
parallelism, and orthogonality. Our code integrates seamlessly with existing
point-based Structure-from-Motion methods and can leverage their 3D points to
further improve the line reconstruction. Furthermore, as a byproduct, the
method is able to recover 3D association graphs between lines and points /
vanishing points (VPs). In thorough experiments, we show that LIMAP
significantly outperforms existing approaches for 3D line mapping. Our robust
3D line maps also open up new research directions. We show two example
applications: visual localization and bundle adjustment, where integrating
lines alongside points yields the best results. Code is available at
https://github.com/cvg/limap.
