3D Delaunay Refinement of Sharp Domains without a Local Feature Size Oracle

2011 ◽

Vol 21 (05) ◽

pp. 507-543

Author(s):

ALEXANDER RAND ◽

NOEL WALKINGTON

Keyword(s):

Delaunay Triangulation ◽

Mesh Generation ◽

Piecewise Linear ◽

Local Information ◽

Local Feature ◽

Delaunay Refinement ◽

Linear Complex ◽

Feature Size ◽

2D And 3D ◽

Local Feature Size

We present Delaunay refinement algorithms for estimating local feature size on the input vertices of a 2D piecewise linear complex and on the input vertices and segments of a 3D piecewise linear complex. These algorithms are designed to eliminate the need for a local feature size oracle during quality mesh generation of domains containing acute input angles. In keeping with Ruppert's algorithm, encroachment in these algorithms can be determined through only local information in the current Delaunay triangulation. The algorithms are practical to implement and several examples are given.

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Adaptive Metrics for Adaptive Samples

Algorithms ◽

10.3390/a13080200 ◽

2020 ◽

Vol 13 (8) ◽

pp. 200

Author(s):

Nicholas J. Cavanna ◽

Donald R. Sheehy

Keyword(s):

Euclidean Space ◽

Surface Reconstruction ◽

Critical Points ◽

Adaptive Sampling ◽

Local Feature ◽

Distance Functions ◽

Feature Size ◽

Homology Inference ◽

Definition Of ◽

Local Feature Size

We generalize the local-feature size definition of adaptive sampling used in surface reconstruction to relate it to an alternative metric on Euclidean space. In the new metric, adaptive samples become uniform samples, making it simpler both to give adaptive sampling versions of homological inference results and to prove topological guarantees using the critical points theory of distance functions. This ultimately leads to an algorithm for homology inference from samples whose spacing depends on their distance to a discrete representation of the complement space.

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A New Surface Reconstruction Algorithm for Noisy 3D Unorganized Points

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.217-219.1312 ◽

2012 ◽

Vol 217-219 ◽

pp. 1312-1317

Author(s):

Jun Song

Keyword(s):

Surface Reconstruction ◽

Point Cloud ◽

Sampling Method ◽

Reconstruction Algorithm ◽

Improved Method ◽

Feature Size ◽

Data Set ◽

Good Surface ◽

Fuzzy C Means Clustering ◽

Local Feature Size

This paper puts forward a new method of surface reconstruction. Power crust algorithm can reconstruct a good surface that is topological valid and be proved theoretically. But when the point cloud is noisy, the surface reconstructed is not good and its running time is long. This paper proposes a improved method of fuzzy c-means clustering to delete the noisy points and a non-uniformly sampling method to resample the input data set according to the local feature size before reconstruction. Experimental results show that the efficiency of the algorithm has been improved much more.

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A Efficient Surface Reconstruction Method for Noisy Samples Based on Bilateral Filtering and down Sampling

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.950.145 ◽

2014 ◽

Vol 950 ◽

pp. 145-149

Author(s):

Wen Rui Wan

Keyword(s):

Surface Reconstruction ◽

Sampling Method ◽

Reconstruction Algorithm ◽

Bilateral Filter ◽

Reconstruction Method ◽

Bilateral Filtering ◽

Triangle Mesh ◽

Feature Size ◽

Original Surface ◽

Local Feature Size

Surface reconstruction is a hot topic in the field of computer graphics. Power Crust algorithm can reconstruct a triangle mesh that is topologically valid and convergent to the original surface. But it can not handle the points with noised and its running time is long. In this paper an efficient surface reconstruction algorithm for noisy samples is proposed. Firstly, we delete the noise by bilateral filter. Secondly, a non-uniformly sampling method is used to resample the input data in order decrease the number of the samples to the local feature size before reconstruction. Finally, Power crust algorithm is be used to reconstructed the surface. From the experiments, it can be seen the speed of reconstruction is increased and the features of the surface are preserved.

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Selective Integration of Local-Feature Detector by Boosting for Pedestrian Detection

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.129.512 ◽

2009 ◽

Vol 129 (3) ◽

pp. 512-521

Author(s):

Kenji Nishida ◽

Takio Kurita

Keyword(s):

Pedestrian Detection ◽

Local Feature ◽

Feature Detector ◽

Selective Integration

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A New Approach for SRAM Soft Defect Root Cause Identification

ISTFA 2007: Conference Proceedings from the 33rd International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2007p0014 ◽

2007 ◽

Author(s):

Peter Egger ◽

Stefan Müller ◽

Martin Stiftinger

Keyword(s):

Integrated Circuits ◽

Top Down ◽

Feature Size ◽

Design Rules ◽

New Approach ◽

Root Cause ◽

Wafer Fab ◽

Sram Cell ◽

Tcad Simulation ◽

Root Cause Identification

Abstract With shrinking feature size of integrated circuits traditional FA techniques like SEM inspection of top down delayered devices or cross sectioning often cannot determine the physical root cause. Inside SRAM blocks the aggressive design rules of transistor parameters can cause a local mismatch and therefore a soft fail of a single SRAM cell. This paper will present a new approach to identify a physical root cause with the help of nano probing and TCAD simulation to allow the wafer fab to implement countermeasures.

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