publication . Preprint . Part of book or chapter of book . Other literature type . 2018

3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration

Yew, Zi Jian; Lee, Gim Hee;
Open Access English
  • Published: 24 Jul 2018
Abstract
Comment: 17 pages, 6 figures. Accepted in ECCV 2018
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download fromView all 3 versions
http://arxiv.org/pdf/1807.0941...
Part of book or chapter of book
Provider: UnpayWall
http://dx.doi.org/10.1007/978-...
Other literature type . 2018
Provider: Datacite
35 references, page 1 of 3

1. Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: Netvlad: Cnn architecture for weakly supervised place recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5297{5307 (2016). https://doi.org/10.1109/CVPR.2016.572

2. Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) European Conference on Computer Vision (ECCV). pp. 404{417. Springer Berlin Heidelberg, Berlin, Heidelberg (2006). https://doi.org/10.1007/11744023 32

3. Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 14(2), 239{256 (1992). https://doi.org/10.1109/34.121791 [OpenAIRE]

4. Bromley, J., Guyon, I., LeCun, Y., Sackinger, E., Shah, R.: Signature veri cation using a \siamese" time delay neural network. In: Advances in Neural Information Processing Systems. pp. 737{744 (1994)

5. Chen, H., Bhanu, B.: 3d free-form object recognition in range images using local surface patches. In: International Conference on Pattern Recognition (ICPR). vol. 3, pp. 136{139 (2004). https://doi.org/10.1109/ICPR.2004.1334487

6. Deng, H., Birdal, T., Ilic, S.: Ppfnet: Global context aware local features for robust 3d point matching. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

7. Dube, R., Dugas, D., Stumm, E., Nieto, J., Siegwart, R., Cadena, C.: Segmatch: Segment based loop-closure for 3d point clouds. arXiv preprint arXiv:1609.07720 (2016)

8. Elbaz, G., Avraham, T., Fischer, A.: 3d point cloud registration for localization using a deep neural network auto-encoder. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2472{2481 (2017). https://doi.org/10.1109/CVPR.2017.265

9. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3354{3361 (2012). https://doi.org/10.1109/CVPR.2012.6248074

10. Gordo, A., Almazan, J., Revaud, J., Larlus, D.: Deep image retrieval: Learning global representations for image search. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) European Conference on Computer Vision (ECCV). pp. 241{ 257. Springer International Publishing (2016). https://doi.org/10.1007/978-3-319- 46466-4 15

11. Hansch, R., Weber, T., Hellwich, O.: Comparison of 3d interest point detectors and descriptors for point cloud fusion. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2(3), 57 (2014)

12. Holz, D., Ichim, A.E., Tombari, F., Rusu, R.B., Behnke, S.: Registration with the point cloud library: A modular framework for aligning in 3-d. IEEE Robotics Automation Magazine 22(4), 110{124 (Dec 2015). https://doi.org/10.1109/MRA.2015.2432331

13. Johnson, A.E., Hebert, M.: Using spin images for e cient object recognition in cluttered 3d scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 21(5), 433{449 (1999). https://doi.org/10.1109/34.765655

14. Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3128{3137 (2015). https://doi.org/10.1109/CVPR.2015.7298932

15. Khoury, M., Zhou, Q.Y., Koltun, V.: Learning compact geometric features. In: International Conference on Computer Vision (ICCV). pp. 153{161 (2017). https://doi.org/10.1109/ICCV.2017.26

35 references, page 1 of 3
Abstract
Comment: 17 pages, 6 figures. Accepted in ECCV 2018
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download fromView all 3 versions
http://arxiv.org/pdf/1807.0941...
Part of book or chapter of book
Provider: UnpayWall
http://dx.doi.org/10.1007/978-...
Other literature type . 2018
Provider: Datacite
35 references, page 1 of 3

1. Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: Netvlad: Cnn architecture for weakly supervised place recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5297{5307 (2016). https://doi.org/10.1109/CVPR.2016.572

2. Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) European Conference on Computer Vision (ECCV). pp. 404{417. Springer Berlin Heidelberg, Berlin, Heidelberg (2006). https://doi.org/10.1007/11744023 32

3. Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 14(2), 239{256 (1992). https://doi.org/10.1109/34.121791 [OpenAIRE]

4. Bromley, J., Guyon, I., LeCun, Y., Sackinger, E., Shah, R.: Signature veri cation using a \siamese" time delay neural network. In: Advances in Neural Information Processing Systems. pp. 737{744 (1994)

5. Chen, H., Bhanu, B.: 3d free-form object recognition in range images using local surface patches. In: International Conference on Pattern Recognition (ICPR). vol. 3, pp. 136{139 (2004). https://doi.org/10.1109/ICPR.2004.1334487

6. Deng, H., Birdal, T., Ilic, S.: Ppfnet: Global context aware local features for robust 3d point matching. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

7. Dube, R., Dugas, D., Stumm, E., Nieto, J., Siegwart, R., Cadena, C.: Segmatch: Segment based loop-closure for 3d point clouds. arXiv preprint arXiv:1609.07720 (2016)

8. Elbaz, G., Avraham, T., Fischer, A.: 3d point cloud registration for localization using a deep neural network auto-encoder. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2472{2481 (2017). https://doi.org/10.1109/CVPR.2017.265

9. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3354{3361 (2012). https://doi.org/10.1109/CVPR.2012.6248074

10. Gordo, A., Almazan, J., Revaud, J., Larlus, D.: Deep image retrieval: Learning global representations for image search. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) European Conference on Computer Vision (ECCV). pp. 241{ 257. Springer International Publishing (2016). https://doi.org/10.1007/978-3-319- 46466-4 15

11. Hansch, R., Weber, T., Hellwich, O.: Comparison of 3d interest point detectors and descriptors for point cloud fusion. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2(3), 57 (2014)

12. Holz, D., Ichim, A.E., Tombari, F., Rusu, R.B., Behnke, S.: Registration with the point cloud library: A modular framework for aligning in 3-d. IEEE Robotics Automation Magazine 22(4), 110{124 (Dec 2015). https://doi.org/10.1109/MRA.2015.2432331

13. Johnson, A.E., Hebert, M.: Using spin images for e cient object recognition in cluttered 3d scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 21(5), 433{449 (1999). https://doi.org/10.1109/34.765655

14. Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3128{3137 (2015). https://doi.org/10.1109/CVPR.2015.7298932

15. Khoury, M., Zhou, Q.Y., Koltun, V.: Learning compact geometric features. In: International Conference on Computer Vision (ICCV). pp. 153{161 (2017). https://doi.org/10.1109/ICCV.2017.26

35 references, page 1 of 3
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