Floating Scale Surface Reconstruction SIGGRAPH 2014

2014/06/11ked

Authors

Simon Fuhrmann, Michael Goesele

Outline

Problems of 3D surface reconstruction Floating scale implicit function Sampling Results

Outline

Problems of 3D surface reconstruction Floating scale implicit function Sampling Results

Problems – noise & reflection

Problems – several scans

Point cloud to 3D surface

Reconstruction

Example

384 photos 196 million samples Most: low resolution Lion: close-up

Properties of this paper

Input: Large, redundant and potentially noisy point sets

(with normals)

Parameters: Scan scale

Output: Implicit function with continuous scale

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

Outline

Problems of 3D surface reconstruction Floating scale implicit function Sampling Results

Implicit surface

Implicit surface

Implicit function

: basis function : weighting function : confidence (c = 1) : ith sample

Basis function

Basis function – 2D illustration

Basis function – 2D illustration

Basis function – 2D illustration

Weighting function

Weighting function – 2D illustration

Analysis in 2D – scale factor

Scale factor = 0.5, 1.0, 2.0

Analysis in 2D – samples

Samples = 50, 500, 5000

Outline

Problems of 3D surface reconstruction Floating scale implicit function Sampling Results

Scale sampling

Scale selection

Scale sampling

Scale selection Determine a cut-off scale value

Local sampling

Local construction

Outline

Problems of 3D surface reconstruction Floating scale implicit function Sampling Results

Basis function / weighting function

Signed distance ramps (basis function)

B-spline (weighting function)

Incomplete data

Input

Performance

Fountain

Thx.