Mesh Edge Detection and Sharp Edge Reconstruction

Speaker:Ma HaoDiSep. 27, 2007

Author

Markus Gross: A professor of computer science, chair of the institute of computational science, and director of the Computer Graphics Laboratory of the Swiss Federal Institute of Technology (ETH) in Zürich. Gross was a papers co-chair of the IEEE Visualization '99, the Eurographics 2000 and the IEEE Visualization 2002 conferences. He was chair of the papers committee of ACM SIGGRAPH 2005. His research interests include point-based graphics, physically-based modeling, multiresolution analysis, and virtual reality.

Author

An Assistant Professor at the Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong.

(1998) Mechatronics Engineering from Huazhong University of Science and Technology,

Ph.D. (2002) in Mechanical Engineering from the Hong Kong University of Science and Technology. A member of IEEE and ASME.

Charlie C. L. Wang( 王昌凌 ):

Reference Incremental reconstruction of shar

p edges on mesh surfaces Charlie C.L. Wang *

Computer-Aided Design 38 (2006) 689–702

Multiresolution Feature Extraction for Unstructured MeshesAndreas Hubeli, Markus Gross

IEEE Visusualization 01, 2001

Background sharp edges and corners are degrad

ed on the resultant surface of evolution: subdivision; restructuring;fairing

Motivation Reconstruction or retain feature info

rmation including sharp edges or ridge lines.

Mesh Edge Detection

Methodology Classification Phase

Selection of Feature Edges

Patch Construction

Skeletonizing

Methodology

Second Order Difference(SOD)

Extended Second Order Difference(ESOD)

Best Fit Polynomial(BFP)

Best Fit Polynomial(BFP)

Angle Between Best Fit Polynomials(ABBFP)

Angle Between Best Fit Polynomials(ABBFP)

Results(ABBFP:different support)

Detection Phase

Detection Phase First, a subset of feature edges is con

structed. Hysteresis Thresholding

Next, Construction of the Patches Finally, the line-type features are ext

racted line-type features are extracted using a

skeletonizingalgorithm

Detection Phase:(step 1 2)

Detection Phase:(step 2 3)

Detection Phase:(step 2 3) for all edges e in patch if (isBoundaryEdge(e) == true) edgeList.insert(e);

while edgeList is not empty do { e = edgeList.front(); // Retrieve the first edge edgeList.pop_front(); // Remove it from the list if(belongsToPatch(e) == false) { removeFromPatch(e); edgeList.insert(newBoundaryEdges); } } }

Results

Reconstruction of Sharp Edge

Methodology Signals indicating sharpness

UUSOD

Surface sharp edges reconstrction Geometry predictor

Uniformly Supported Second-Order Difference(USSOD)

where d(v,f) returns the Euclidean distance from v to the pointset of f (i.e. not the plane holding f).

P(f i,f j)representing the inner product of unit normal

vectors on two faces ? (uniform support size)

Uniformly Supported Second-Order Difference(USSOD)

Better but not completely solve

Uniformly Supported Second-Order Difference(USSOD)

Geometry predictor Some definition

static vertices sharp vertex static triangle ( all static vertices ) dynamic triangles( one sharp vertices )

Geometry predictor An ideal position for a vertex v

minimizes the difference between its position and the smoothness signals—tangent planes:

contains the static triangles near the vertex v is the unit normal vector of f is a point on the static triangle f

Geometry predictor

Progressive surface prediction

red circles represent sharp vertices and white circles denote static vertices.

Results

Results

Results

Results

Limitations The feature that blends smoothly into a flat

area may be miss-sharpened Some unwanted sharpening will be given o

n small radius Not a adaptive sharpness identification tec

hniques

Results

THE END