Semi-Global 3D Line Modeling for Incremental Structure-from-Motion
In Proceedings British Machine Vision Conference 2014
AbstractStructure-from-Motion (SfM) approaches, which are conventionally based on local interest point matches, tend to work well for richly textured indoor- and outdoor environments. However, in less textured scene areas the density of the resulting point cloud suffers from the lower number of matchable interest points. This significantly affects subsequent computer vision tasks like image based localization, surface extraction or visual navigation. In this paper, we propose a novel 3D reconstruction approach that increases the amount of 3D information in the reconstruction by exploiting line segments as complementary features. We introduce an efficient and effective semi-global approach, which takes into account local (per 2D line segment) as well as global (graph clustering) 3D line hypotheses constellations. Our approach outperforms the state-of-the-art in terms of accuracy, with comparable runtime.
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