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Journals >
Chinese Journal of Lasers >
Volume 47 >
Issue 12 >
Page 1204007 > Article
Chinese Journal of Lasers
Vol. 47, Issue 12, 1204007 (2020)
Inverse Depth Adaptive Weighting Based Multi-View Triangulation Method
Fang Wei
1、*
and Yang Kui
2
Author Affiliations
1
School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
2
School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China
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DOI:
10.3788/CJL202047.1204007
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Fang Wei, Yang Kui. Inverse Depth Adaptive Weighting Based Multi-View Triangulation Method[J]. Chinese Journal of Lasers, 2020, 47(12): 1204007
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Fig. 1.
Schematic diagram of triangulation description
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Fig. 2.
Implementation process of our method
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Fig. 3.
Synthetic datasets for multi-view triangulation. (a) Type A; (b) type B; (c) type C
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Fig. 4.
Iteration performance at different noise levels
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Fig. 5.
Multi-view triangulation results of our method under the public datasets. (a) Lund Cathedral; (b) Aos Hus; (c) San Marco; (d) Orebro Castle; (e) Buddah Statue; (f) East Indiaman Goteborg; (g) Ystad Monestary; (h) Round Church; (i) Skansen Kronan; (j) Skansen Lejonet
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Fig. 6.
Number of iterations of our method for multi-view triangulation in public datasets
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σ
/pixel
Method
Type A
Type B
Type C
Time /s
3D error /
m
2D error /
pixel
Time /s
3D error /
m
2D error /
pixel
Time /s
3D error /
m
2D error /
pixel
Midpoint
0.648
0.023
6.107
2.521
0.021
5.945
0.853
0.017
5.825
6
Ours
1.761
0.017
5.728
5.971
0.018
5.938
2.251
0.016
5.801
L
2
Rep
2.996
0.015
5.704
11.668
0.017
5.936
4.005
0.016
5.797
Midpoint
0.617
0.045
12.244
2.462
0.059
11.941
0.810
0.036
11.652
12
Ours
1.802
0.031
11.669
5.675
0.037
11.883
2.068
0.031
11.595
L
2
Rep
2.841
0.030
11.620
11.678
0.035
11.879
3.933
0.029
11.582
Midpoint
0.603
0.094
25.953
2.578
0.208
24.278
0.849
0.078
23.498
24
Ours
1.914
0.063
23.009
7.191
0.074
23.781
2.382
0.064
23.216
L
2
Rep
3.224
0.059
22.905
14.965
0.071
23.773
4.017
0.059
23.180
Table 1.
Results obtained by the multi-view triangulation method based on simulation data
Dataset
Time /s
Mean 2D reprojection errors /pixel
ID
View
Point
Midpoint
Ours
L
2
Rep
Mean
Variance
Midpoint
Ours
L
2
Rep
Midpoint
Ours
L
2
Rep
1
1208
159055
23.082
43.014
91.910
1.088
1.078
1.077
0.217
0.206
0.205
2
800
354134
22.303
45.273
93.063
0.816
0.805
0.805
0.302
0.289
0.287
3
1498
231507
33.786
66.944
134.205
0.807
0.799
0.798
0.331
0.316
0.315
4
761
53857
10.998
20.410
40.532
0.942
0.936
0.936
0.196
0.191
0.190
5
322
156356
9.440
15.233
31.873
0.651
0.649
0.649
0.270
0.266
0.265
6
179
25655
2.723
5.736
11.040
1.127
1.122
1.121
0.386
0.377
0.376
7
290
139951
14.520
26.328
53.460
0.970
0.968
0.967
0.170
0.169
0.168
8
92
84643
6.366
11.699
22.445
0.387
0.385
0.385
0.121
0.119
0.119
9
131
28371
3.812
7.254
14.793
0.807
0.802
0.802
0.175
0.170
0.169
10
368
74423
8.370
15.871
34.350
1.032
1.023
1.022
0.209
0.202
0.200
Table 2.
Results of different methods based on Lund's public dataset
ID
Time /s
Mean 2D reprojection error /pixel
Midpoint
Ours
L
2
Rep
Midpoint
Ours
L
2
Rep
1
20.508
44.275
90.556
2.136
2.105
2.103
2
23.829
46.673
96.919
2.964
2.934
2.930
3
34.913
63.022
139.873
2.663
2.627
2.624
4
11.380
20.585
46.909
1.677
1.664
1.662
5
9.691
17.316
38.984
3.050
3.046
3.044
6
2.724
5.838
11.809
2.356
2.350
2.351
7
13.903
27.213
58.175
2.257
2.241
2.236
8
6.289
12.397
26.147
2.829
2.813
2.811
9
3.859
6.827
15.900
1.917
1.871
1.870
10
7.786
15.550
34.184
2.139
2.109
2.105
Table 3.
Experimental results of different methods on the Lund public dataset (
σ
=5 pixel)
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Fang Wei, Yang Kui. Inverse Depth Adaptive Weighting Based Multi-View Triangulation Method[J]. Chinese Journal of Lasers, 2020, 47(12): 1204007
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Paper Information
Category: Measurement and metrology
Received: Jun. 18, 2020
Accepted: --
Published Online: Dec. 1, 2020
The Author Email: Wei Fang (fangwei@bupt.edu.cn)
DOI:
10.3788/CJL202047.1204007
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