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
this https URL

本文介绍了一种名为 LIMAP 的库，用于从多视角图像中稳健高效地创建 3D 线路图，该库通过重新审视线三角化的退化问题、精心制作的得分和跟踪构建以及利用线 / 点的结构先验知识来实现。在全面实验中，我们展示了 LIMAP 在 3D 线路图上显着优于现有方法，并且我们的稳健 3D 线路图也开辟了新的研究方向：视觉本地化和束调整。