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Discovering Structured Variations Via Template Matching
(© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017)
Understanding patterns of variation from raw measurement data remains a central goal of shape analysis. Such an understanding reveals which elements are repeated, or how elements can be derived as structured variations ...
Symmetry in 3D Geometry: Extraction and Applications
(The Eurographics Association and Blackwell Publishing Ltd., 2013)
The concept of symmetry has received significant attention in computer graphics and computer vision research in recent years. Numerous methods have been proposed to find, extract, encode and exploit geometric symmetries ...
Symmetry in 3D Geometry: Extraction and Applications
(The Eurographics Association, 2012)
The concept of symmetry has received significant attention in computer graphics and computer vision research in recent years. Numerous methods have been proposed to find and extract geometric symmetries and exploit such ...
Factored Facade Acquisition using Symmetric Line Arrangements
(The Eurographics Association and John Wiley and Sons Ltd., 2012)
We introduce a novel framework for image-based 3D reconstruction of urban buildings based on symmetry priors. Starting from image-level edges, we generate a sparse and approximate set of consistent 3D lines. These lines ...
Uncertainty and Variability in Point Cloud Surface Data
(The Eurographics Association, 2004)
We 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 ...
Example-Based 3D Scan Completion
(The Eurographics Association, 2005)
We present a novel approach for obtaining a complete and consistent 3D model representation from incomplete surface scans, using a database of 3D shapes to provide geometric priors for regions of missing data. Our method ...
Probabilistic Fingerprints for Shapes
(The Eurographics Association, 2006)
We propose a new probabilistic framework for the efficient estimation of similarity between 3D shapes. Our framework is based on local shape signatures and is designed to allow for quick pruning of dissimilar shapes, while ...