  Although semi-dense Simultaneous Localization and Mapping (SLAM) has been
becoming more popular over the last few years, there is a lack of efficient
methods for representing and processing their large scale point clouds. In this
paper, we propose using 3D line segments to simplify the point clouds generated
by semi-dense SLAM. Specifically, we present a novel incremental approach for
3D line segment extraction. This approach reduces a 3D line segment fitting
problem into two 2D line segment fitting problems and takes advantage of both
images and depth maps. In our method, 3D line segments are fitted incrementally
along detected edge segments via minimizing fitting errors on two planes. By
clustering the detected line segments, the resulting 3D representation of the
scene achieves a good balance between compactness and completeness. Our
experimental results show that the 3D line segments generated by our method are
highly accurate. As an application, we demonstrate that these line segments
greatly improve the quality of 3D surface reconstruction compared to a feature
point based baseline.
