Abstract
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.
Original language | English |
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Title of host publication | Proceedings of the International Conference on 3D Vision |
Pages | 535-542 |
Publication status | Published - 2014 |
Event | International Conference on 3D Vision: 3DV 2014 - Tokyo, Japan Duration: 8 Dec 2014 → 11 Dec 2014 |
Conference
Conference | International Conference on 3D Vision |
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Country/Territory | Japan |
City | Tokyo |
Period | 8/12/14 → 11/12/14 |
Fields of Expertise
- Information, Communication & Computing
Treatment code (Nähere Zuordnung)
- Application