This paper presents a very simple but efficient algorithm for 3D line segment
detection from large scale unorganized point cloud. Unlike traditional methods
which usually extract 3D edge points first and then link them to fit for 3D
line segments, we propose a very simple 3D line segment detection algorithm
based on point cloud segmentation and 2D line detection. Given the input
unorganized point cloud, three steps are performed to detect 3D line segments.
Firstly, the point cloud is segmented into 3D planes via region growing and
region merging. Secondly, for each 3D plane, all the points belonging to it are
projected onto the plane itself to form a 2D image, which is followed by 2D
contour extraction and Least Square Fitting to get the 2D line segments. Those
2D line segments are then re-projected onto the 3D plane to get the
corresponding 3D line segments. Finally, a post-processing procedure is
proposed to eliminate outliers and merge adjacent 3D line segments. Experiments
on several public datasets demonstrate the efficiency and robustness of our
method. More results and the C++ source code of the proposed algorithm are
publicly available at this https URL

本文提出了一种用于大规模无序点云的三维线段检测的简单而高效的算法，基于点云分割和二维线段检测，能够有效地提取三维线段，以消除离群点和合并相邻的三维线段。

基于无序点云的快速 3D 线段检测

Fast 3D Line Segment Detection From Unorganized Point Cloud

We show that by extending the Laplacian formalism, which was first introduced
in the Graphics community to regularize 3D meshes, we can turn the monocular 3D
shape reconstruction of a deformable surface given correspondences with a
reference image into a much better-posed problem. This allows us to quickly and
reliably eliminate outliers by simply solving a linear least squares problem.
This yields an initial 3D shape estimate, which is not necessarily accurate,
but whose 2D projections are. The initial shape is then refined by a
constrained optimization problem to output the final surface reconstruction.
Our approach allows us to reduce the dimensionality of the surface
reconstruction problem without sacrificing accuracy, thus allowing for
real-time implementations.

通过扩展 Laplacian formalism，我们可以将单目 3D 形状重建转化为更好处理的问题，通过简单地解决线性最小二乘问题可以快速可靠地消除异常值。这使我们能够实时地降低表面重建问题的维度而不会损失准确性。