Symposium on Point Based Graphics 04
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Browsing Symposium on Point Based Graphics 04 by Subject "Categories and Subject Descriptors (according to ACM CCS): I.3.5. [Computer Graphics]: Computational Geometry and Object Modeling"
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Item Post-processing of Scanned 3D Surface Data(The Eurographics Association, 2004) Weyrich, T.; Pauly, M.; Keiser, R.; Heinzle, S.; Scandella, S.; Gross, M.; Markus Gross and Hanspeter Pfister and Marc Alexa and Szymon Rusinkiewicz3D shape acquisition has become a major tool for creating digital 3D surface data in a variety of application elds. Despite the steady increase in accuracy, most available scanning techniques cause severe scanning artifacts such as noise, outliers, holes, or ghost geometry. To apply sophisticated modeling operations on these data sets, substantial post-processing is usually required. In this paper, we address a variety of scanning artifacts that are created by common optical scanners and provide a comprehensive set of user-guided tools to process corrupted data sets. These include an eraser tool, low-pass lter s for noise removal, a set of outlier detection methods, and various up-sampling and hole- lling tools. These techniques can be applied early in the content creation pipeline. Therefore, all our tools are implemented to operate directly on the acquired point cloud. We also emphasize the need for extensive user control and an ef cient visual feedback loop. The effectiveness of our scan cleaning tools is demonstrated on various models acquired with commercial laser-range scanners and low-cost structured light scanners.Item Uncertainty and Variability in Point Cloud Surface Data(The Eurographics Association, 2004) Pauly, Mark; Mitra, Niloy J.; Guibas, Leonidas J.; Markus Gross and Hanspeter Pfister and Marc Alexa and Szymon RusinkiewiczWe present a framework for analyzing shape uncertainty and variability in point-sampled geometry. Our representation is mainly targeted towards discrete surface data stemming from 3D acquisition devices, where a finite number of possibly noisy samples provides only incomplete information about the underlying surface. We capture this uncertainty by introducing a statistical representation that quantifies for each point in space the likelihood that a surface fitting the data passes through that point. This likelihood map is constructed by aggregating local linear extrapolators computed from weighted least squares fits. The quality of fit of these extrapolators is combined into a corresponding confidence map that measures the quality of local tangent estimates. We present an analysis of the effect of noise on these maps, show how to efficiently compute them, and extend the basic definition to a scale-space formulation. Various applications of our framework are discussed, including an adaptive re-sampling method, an algorithm for reconstructing surfaces in the presence of noise, and a technique for robustly merging a set of scans into a single point-based representation.