Scale surface reconstruction
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Transcript of Scale surface reconstruction
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Floating Scale Surface Reconstruction SIGGRAPH 2014
2014/06/11ked
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Authors
Simon Fuhrmann, Michael Goesele
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Outline
Problems of 3D surface reconstruction Floating scale implicit function Sampling Results
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Outline
Problems of 3D surface reconstruction Floating scale implicit function Sampling Results
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Problems – noise & reflection
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Problems – several scans
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Point cloud to 3D surface
Reconstruction
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Example
384 photos 196 million samples Most: low resolution Lion: close-up
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Properties of this paper
Input: Large, redundant and potentially noisy point sets
(with normals)
Parameters: Scan scale
Output: Implicit function with continuous scale
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Properties of this paper
Input: Large, redundant and potentially noisy point sets
(with normals)
Parameters: Scan scale (patch size in a multi-view stereo alg.)
Output: Implicit function with continuous scale
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Outline
Problems of 3D surface reconstruction Floating scale implicit function Sampling Results
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Implicit surface
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Implicit surface
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Implicit function
: basis function : weighting function : confidence (c = 1) : ith sample
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Basis function
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Basis function – 2D illustration
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Basis function – 2D illustration
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Basis function – 2D illustration
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Weighting function
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Weighting function – 2D illustration
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Analysis in 2D – scale factor
Scale factor = 0.5, 1.0, 2.0
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Analysis in 2D – samples
Samples = 50, 500, 5000
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Outline
Problems of 3D surface reconstruction Floating scale implicit function Sampling Results
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Scale sampling
Scale selection
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Scale sampling
Scale selection Determine a cut-off scale value
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Local sampling
Local construction
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Outline
Problems of 3D surface reconstruction Floating scale implicit function Sampling Results
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Basis function / weighting function
Signed distance ramps (basis function)
B-spline (weighting function)
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Incomplete data
Input
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Performance
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Fountain
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Thx.