• Laser & Optoelectronics Progress
  • Vol. 59, Issue 22, 2215002 (2022)
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 Cite this Article Set citation alerts
    Tao Long, Chang Su, Jian Wang. Learning Feature Point Descriptors for Detail Preservation[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2215002 Copy Citation Text show less
    References

    [1] Cadena C, Carlone L, Carrillo H et al. Past, present, and future of simultaneous localization and mapping: toward the robust-perception age[J]. IEEE Transactions on Robotics, 32, 1309-1332(2016).

    [2] Agarwal S, Snavely N, Seitz S M et al. Bundle adjustment in the large[M]. Daniilidis K, Maragos P, Paragios N. Computer vision-ECCV 2010. Lecture notes in computer science, 6312, 29-42(2010).

    [3] Wang S, Clark R, Wen H K et al. DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks[C], 2043-2050(2017).

    [4] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 60, 91-110(2004).

    [5] Bay H, Ess A, Tuytelaars T et al. Speeded-up robust features (SURF)[J]. Computer Vision and Image Understanding, 110, 346-359(2008).

    [6] Rublee E, Rabaud V, Konolige K et al. ORB: an efficient alternative to SIFT or SURF[C], 2564-2571(2011).

    [7] Leutenegger S, Chli M, Siegwart R Y. BRISK: binary robust invariant scalable keypoints[C], 2548-2555(2011).

    [8] Balntas V, Lenc K, Vedaldi A et al. HPatches: a benchmark and evaluation of handcrafted and learned local descriptors[C], 3852-3861(2017).

    [9] Yi K M, Trulls E, Lepetit V et al. LIFT: learned invariant feature transform[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9910, 467-483(2016).

    [10] Jaderberg M, Simonyan K, Zisserman A et al. Spatial transformer networks[C], 2017-2025(2015).

    [11] Ono Y, Trulls E, Fua P et al. LF-Net: learning local features from images[C], 6237-6247(2018).

    [12] DeTone D, Malisiewicz T, Rabinovich A. SuperPoint: self-supervised interest point detection and description[C], 337-33712(2018).

    [13] Christiansen P H, Kragh M F, Brodskiy Y et al. UnsuperPoint: end-to-end unsupervised interest point detector and descriptor[EB/OL]. https://arxiv.org/abs/1907.04011

    [14] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

    [15] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [16] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

    [17] Tang J X, Kim H, Guizilini V et al. Neural outlier rejection for self-supervised keypoint learning[EB/OL]. https://arxiv.org/abs/1912.10615v1

    [18] Lin T Y, Maire M, Belongie S et al. Microsoft COCO: common objects in context[M]. Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science, 8693, 740-755(2014).

    [19] Schmid C, Mohr R, Bauckhage C. Evaluation of interest point detectors[J]. International Journal of Computer Vision, 37, 151-172(2000).

    [20] Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 24, 381-395(1981).

    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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