3D Reconstruction of Interior Wall Surfaces Under Occlusion and Clutter
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.
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.
BibTeX
@conference{Oliver-2011-7264,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)},
year = {2011},
month = {May},
keywords = {3D model, laser scanner, point cloud, opening detection, occlusion reasoning},
}