2. OUTLINES
Introduction
Conventional data reduction method
Triangulated model proposed by Chen et all.(1999)
Proposed method of this paper
How this technique works?(Triangulation, Normal
estimation, Initial grid generation, Grid sub-division,
Extraction of points)
Application examples
Benefits and Drawbacks of the proposed methods.
References
3. Reverse Engineering: While conventional engineering
transforms engineering concepts and models into real parts, in
reverse engineering real parts are transformed into engineering
models and concepts.
Capturing device:
1. Contact type;
2. Non-contact type. Figure: Reverse Engineering Process
Laser scanning system:
Non-contact type device. Most widely used for speed and
accuracy. It is useful for surface data collection of complex
shape and free form surfaces.
4. Data reduction---Why?
Amount of data in the raw file is usually very large.
Large storage space is required.
Increase computational time.
So, data reduction is very important in reverse engineering.
This paper proposed a point data reduction method
based on normal values of points using 3D grids. This
method was applied for two models and results were
discussed.
8. Point data:
Structured data are obtained by laser scanning system.
Since a scan path is defined as a series of line
segments, each line in the path is ordered as are the
points in each scan line.
Laser
Scan line
probe
Scan Laser
data set stripe
Sensor Part for
scanning
Figure: Laser scanning system
Figure: Point data
9. Triangulation:
Three vertices of a triangle can not be in one scan line. When
two vertices are in one scan line, the last one must be other.
Figure: Triangulated point data
10. Normal Estimation:
Figure: Two edges of a triangle
Figure: Two cases for edge determination
Figure: Edge generation of two
scan lines
Formula used
13. Extraction of points:
After sub-division, representative points are extracted.
A point whose normal value is closest to the average of
the points within the cell, is selected as the representative
points.
In this method, level of data reduction depends on two
factors---
Number of initial cells
Size of the user defined tolerance
14. Figure: Scanned point data Figure: Point data with normal
Figure: Complete model with bounding box
17. Previously proposed 2D grid methods work only
for data acquired with one scanning direction but
proposed 3D grid method directly deals with entire
3D point data. It does not require merging of data
in advance.
In the phone model example, we have already seen
that 3D grid method shows better results compared
to other three conventional methods.
Resulting cells can be used for volumetric
representations for their shapes. Cross -sectional
slice data needed for rapid prototyping can also be
generated from 3D grid model efficiently.
18. In 2006, Liu Deping et all. Proposed a new method of data
reduction called “Adaptive minimum distance method".
Survey data capturing
Noise points canceling
Curvature calculation
and analysis
Data points zero division
Data reduction
Figure: Adaptive minimum distance method
19. Noise elimination in data samples is an important issue. In our
current paper, we have no indication of noise filtering process.
But Liu Deping et all. showed noise filtering by visual
observation method, curve check method and chord high
difference method.
Data points are divided into sudden zero(sharp change),
transition zero(change of curvature is the bigger), flat
zero(change of curvature is gentle).Less data points are
preserved at the less curvature zero and more points are
preserved at the bigger curvature zero or sharp zero.
This method better preserves the detail character of initial
data(precision) and also improves the data reduction efficiency.
20. REFERENCES
K.H. Lee, H. Woo and T. Suk, “Point data reduction using 3D
grids”, The International Journal of Advanced Manufacturing
Technology, 2001.
Y.H Chen, C.T Ng and Y.Z wang, “Data reduction in
integrated reverse Engineering and rapid prototyping”,
International Journal of Computer Integrated Manufacturing,
12(2), pp. 97-103, 1999.
LIU Deping, CHEN Jianjun and SHANGGUAN Jianlin, “A
study on the point data reduction in reverse engineering”,
International Technology and Innovation Conference 2006.
