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.

翻译：与稀疏关键点不同，少量线段即可简洁地编码场景的高层布局，因其常勾勒出主要结构元素。线段除提供强几何线索外，在城市景观和室内场景中也无处不在。尽管具有明显优势，当前基于线段的二维重建方法仍远落后于基于点的方法。本文旨在通过引入LIMAP（三维线段建图库）来缩小这一差距，该库能够鲁棒高效地从多视图影像中生成三维线段地图。这通过重新审视线段三角化的退化问题、精心设计评分与轨迹构建、并利用线段共面、平行与正交等结构先验得以实现。我们的代码可与现有基于点的运动恢复结构方法无缝集成，并能利用其三维点进一步改进线段重建。此外，该方法作为副产品，还能恢复线段与点/灭点之间的三维关联图。在全面实验中，我们展示了LIMAP在三维线段建图方面显著优于现有方法。我们鲁棒的三维线段图还开辟了新的研究方向：通过视觉定位与光束法平差两个示例应用证明，将线段与点联合优化可获得最优结果。代码见https://github.com/cvg/limap。