The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Laser scanners are often used to create 3D models of buildings for civil engineering applications. The current manual process is time-consuming and error-prone. This paper presents a method for using laser scanner data to model predominantly planar surfaces, such as walls, floors, and ceilings, despite the presence of significant amounts of clutter and occlusion, which occur frequently in natural indoor environments. Our goal is to recover the surface shape, detect and model any openings, and fill in the occluded regions. Our method identifies candidate surfaces for modeling, labels occluded surface regions, detects openings in each surface using supervised learning, and reconstructs the surface in the occluded regions. We evaluate the method on a large, highly cluttered data set of a building consisting of forty separate rooms.

Keywords

3D model, laser scanner, point cloud, opening detection, occlusion reasoning

Notes

Associated Lab(s) / Group(s): 3D Vision and Intelligent Systems Group

Associated Project(s): Automated Reverse Engineering of Buildings and Detailed Wall Modeling in Cluttered Environments

Note: This material is based upon work supported, in part, by the National Science Foundation under Grant No. 0856558 and by the Pennsylvania Infrastructure Technology Alliance. We thank Quantapoint, Inc., for providing experimental data. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Text Reference

Antonio Adan Oliver and Daniel Huber, "3D Reconstruction of Interior Wall Surfaces Under Occlusion and Clutter," Proceedings of 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), May, 2011.

BibTeX Reference

@inproceedings{Adan_Oliver_2011_6861,
   author = "Antonio {Adan Oliver} and Daniel Huber",
   title = "3D Reconstruction of Interior Wall Surfaces Under Occlusion and Clutter",
   booktitle = "Proceedings of 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)",
   month = "May",
   year = "2011",
   Notes = "This material is based upon work supported, in part, by the National Science Foundation under Grant No. 0856558 and by the Pennsylvania Infrastructure Technology Alliance. We thank Quantapoint, Inc., for providing experimental data. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation."
}

The Robotics Institute

Menu