Improving Sparse 3D Models for Man-Made Environments Using Line-Based 3D Reconstruction

Traditional Structure-from-Motion (SfM) approaches work well for richly textured scenes with a high number of distinctive feature points. Since man-made environments often contain textureless objects, the resulting point cloud suffers from a low density in corresponding scene parts. The missing 3D information heavily affects all kinds of subsequent post-processing tasks (e.g. meshing), and significantly decreases the visual appearance of the resulting 3D model. We propose a novel 3D reconstruction approach, which uses the output of conventional SfM pipelines to generate additional complementary 3D information, by exploiting line segments. We use appearance-less epipolar guided line matching to create a potentially large set of 3D line hypotheses, which are then verified using a global graph clustering procedure. We show that our proposed method outperforms the current state-of-the-art in terms of runtime and accuracy, as well as visual appearance of the resulting reconstructions.