is Research Center Vienna

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Reisner-Kollmann I.,Vienna University of Technology | Maierhofer S.,is Research Center Vienna
Proceedings of the IEEE International Conference on Computer Vision | Year: 2011

Consolidation of point clouds, including denoising, outlier removal and normal estimation, is an important pre-processing step for surface reconstruction techniques. We present a consolidation framework specialized on point clouds created by multiple frames of a depth camera. An adaptive view-dependent locally optimal projection operator denoises multiple depth maps while keeping their structure in two-dimensional grids. Depth cameras produce a systematic variation of noise scales along the depth axis. Adapting to different noise scales allows to remove noise in the point cloud and preserve well-defined details at the same time. Our framework provides additional consolidation steps for depth maps like normal estimation and outlier removal. We show how knowledge about the distribution of noise in the input data can be effectively used for improving point clouds. © 2011 IEEE.


Musialski P.,is Research Center Vienna | Recheis M.,is Research Center Vienna | Maierhofer S.,is Research Center Vienna | Wonka P.,Arizona State University | Purgathofer W.,Vienna University of Technology
Proceedings - SCCG 2010: 26th Spring Conference on Computer Graphics | Year: 2010

Typical building facades consist of regular structures such as windows arranged in a predominantly grid-like manner. We propose a method that handles precisely such facades and assumes that there must be horizontal and vertical repetitions of similar patterns. Using a Monte Carlo sampling approach, this method is able to segment repetitive patterns on orthogonal images along the axes even if the pattern is partially occluded. Additionally, it is very fast and can be used as a preprocessing step for finer segmentation stages. Copyright © 2010 by the Association for Computing Machinery, Inc.

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