Line3D: Efficient 3D Scene Abstraction for the Built Environment
Manuel Höfer, Michael Maurer, Horst Bischof · Lecture notes in computer science · 2015
Extracting 3D information from a moving camera is traditionally based on interest point detection and matching. This is especially challenging in the built environment, where the number of distinctive interest points is naturally limited. While common Structure-from-Motion (SfM) approaches usually manage to obtain the correct camera poses, the number of accurate 3D points is very small due to the low number of matchable features. Subsequent Multi-view Stereo approaches may help to overcome this problem, but suffer from a high computational complexity. We propose a novel approach for the task of 3D scene abstraction, which uses straight line segments as underlying features. We use purely geometric constraints to match 2D line segments from different images, and formulate the reconstruction procedure as a graph-clustering problem. We show that our method generates accurate 3D models, with a low computational overhead compared to SfM alone. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.