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Summary: EG UK Theory and Practice of Computer Graphics (2010)
John Collomosse, Ian Grimstead (Editors)
Surfel Based Geometry Reconstruction
Vedrana Andersen1
, Henrik Aanæs1
and Andreas Bærentzen1
1Technical University of Denmark
Abstract
We propose a method for retrieving a piecewise smooth surface from noisy data. In data acquired by a scanning
process sampled points are almost never on the discontinuities making reconstruction of surfaces with sharp
features difficult. Our method is based on a Markov Random Field (MRF) formulation of a surface prior, with
the surface represented as a collection of small planar patches, the surfels, associated with each data point. The
main advantage of using surfels is that we avoid treating data points as vertices. MRF formulation of the surface
prior allows us to separately model the likelihood (related to the mesh formation process) and the local surface
properties. We chose to model the smoothness by considering two terms: the parallelism between neighboring
surfels, and their overlap. We have demonstrated the feasibility of this approach on both synthetical and scanned
data. In both cases sharp features were precisely located and planar regions smoothed.
Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry
and Object Modeling
1. Introduction
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