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Table 2: Experimental results on noiseless data. The values
in the table are the average of the results of 100 runs of the
algorithm, each time with a newly generated point set P .
model preproc. matching # of calls of the
[s] [s] basic algorithm
head 11.433 21.556 6
bunny 2.433 4.556 7
cow 0.201 1.771 5
cat 0.04 0.951 2
measure1 measure2 measure3
head 0.000748 0.000103 0.0000037
bunny 0.004483 0.000203 0.0000663
cow 0.005954 0.001869 0.0032076
cat 0.009801 0.000899 0.0028853
Table 3: Experimental results on noisy data of the head
model. Random noise, between 0 and noise factor*average
edge length, was added to the points from P . The values
in the table are the average of the results of 100 runs of the
algorithm, each time with a newly generated point set P .
noise preproc. matching # of calls of the
factor [s] [s] basic algorithm
0 11.433 21.556 6
1 11.386 21.281 6
5 11.425 22.771 5
10 11.375 22.609 3
measure1 measure2 measure3
0 0.000748 0.000103 0.0000037
1 0.008512 0.000703 0.0000067
5 0.015954 0.001869 0.0032076
10 0.036983 0.210899 0.0048853
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