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Journals >
Laser & Optoelectronics Progress >
Volume 59 >
Issue 22 >
Page 2215002 > Article
Laser & Optoelectronics Progress
Vol. 59, Issue 22, 2215002 (2022)
Learning Feature Point Descriptors for Detail Preservation
Tao Long, Chang Su, and Jian Wang
*
Author Affiliations
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
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DOI:
10.3788/LOP202259.2215002
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Tao Long, Chang Su, Jian Wang. Learning Feature Point Descriptors for Detail Preservation[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2215002
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Fig. 1.
Neural network architecture of feature point extraction
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Fig. 2.
Schematic diagram of center offset of position prediction
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Fig. 3.
Flow chart of homography estimation
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Fig. 4.
Schematic diagram of homography error calculation
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Fig. 5.
Failure case of baseline feature matching
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Fig. 6.
Qualitative results of proposed method on images pairs on HPatches dataset. (a)Illumination cases.; (b) rotation cases; (c) perspective cases
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Method
Repeat
LE
HA-1
HA-3
HA-5
MS
Baseline
0.633
1.044
0.503
0.796
0.868
0.491
V1
0.675
0.831
0.505
0.822
0.897
0.576
V2
0.676
0.856
0.581
0.866
0.903
0.554
V3
0.669
0.842
0.586
0.871
0.912
0.555
Table 1.
Comparison of experimental results of different network structures
Method
Repeatability rate
Localization error
Low resolution
High resolution
Low resolution
High resolution
ORB
0.532
0.525
1.429
1.430
SURF
0.491
0.468
1.150
1.244
BRISK
0.566
0.505
1.077
1.207
SIFT
0.451
0.421
0.855
1.011
LF-Net(indoor)
0.486
0.467
1.341
1.385
LF-Net(outdoor)
0.538
0.523
1.084
1.183
SuperPoint
0.631
0.593
1.109
1.212
UnsuperPoint
0.645
0.612
0.832
0.991
Proposed method
0.669
0.663
0.842
0.926
Table 2.
Comparison of key point detection performance of different methods
Method
Low resolution,300 points
High resolution,1000 points
HA-1
HA-3
HA-5
MS
HA-1
HA-3
HA-5
MS
ORB
0.131
0.422
0.540
0.218
0.286
0.607
0.71
0.204
SURF
0.397
0.702
0.762
0.255
0.421
0.745
0.812
0.230
BRISK
0.414
0.767
0.826
0.258
0.300
0.653
0.746
0.211
SIFT
0.622
0.845
0.878
0.304
0.602
0.833
0.876
0.265
LF-Net(indoor)
0.183
0.628
0.779
0.326
0.231
0.679
0.803
0.287
LF-Net(outdoor)
0.347
0.728
0.831
0.296
0.400
0.745
0.834
0.241
SuperPoint
0.491
0.833
0.893
0.318
0.509
0.834
0.900
0.281
UnsuperPoint
0.579
0.855
0.903
0.424
0.493
0.843
0.905
0.383
Proposed method
0.586
0.871
0.912
0.555
0.552
0.840
0.916
0.508
Table 3.
Comparison of homography estimation and matching performance of different methods
Method
Hpatches subset
Repeat
LE
HA-1
HA-3
HA-5
MS
Outlier_rejection
[
14
]
ALL
0.686
0.890
0.595
0.871
0.912
0.544
Illumination
0.678
0.826
0.753
0.942
0.984
0.614
Viewpoint
0.693
0.953
0.494
0.801
0.857
0.479
Proposed method
ALL
0.669
0.842
0.586
0.871
0.912
0.555
Illumination
0.643
0.789
0.642
0.933
0.965
0.576
Viewpoint
0.695
0.893
0.532
0.810
0.861
0.534
Table 4.
Comparison of experimental results on different data subsets
Abstract
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Tao Long, Chang Su, Jian Wang. Learning Feature Point Descriptors for Detail Preservation[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2215002
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Paper Information
Category: Machine Vision
Received: Aug. 19, 2021
Accepted: Sep. 24, 2021
Published Online: Nov. 25, 2022
The Author Email: Wang Jian (jianwang@tju.edu.cn)
DOI:
10.3788/LOP202259.2215002
Recommended Topics
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