Abstract
Nowadays, with easily accessible 3D point cloud acquisition tools, the field of point cloud processing gained a lot of attention. Extracting features from 3D data became main computer vision task. In this paper, we reviewed methods of extracting local features from objects represented by point clouds. The goal of the work was to make theoretical overview and evaluation of selected point cloud detectors and descriptors. We performed an experimental assessment of the repeatability and computational efficiency of individual methods using the well known Stanford 3D Scanning Repository database with the aim of identifying a method which is computationally-efficient in finding good corresponding points between two point clouds. We combine the detectors with several feature descriptors and show which combination of detector and descriptor is suitable for object recognition task in cluttered scenes. Our tests show that choosing the right detector impacts the descriptor’s performance in the recognition process. The repeatability tests of the detectors show that the data which contained occlusions have a high impact on their performance. We summarized the results into graphs and described them with respect to the individual tested properties of the methods.
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References
Azimi, S., Lall, B., Gandhi, T.K.: Performance evalution of 3D keypoint detectors and descriptors for plants health classification. In: 2019 16th International Conference on Machine Vision Applications (MVA), pp. 1–6. IEEE (2019)
Belongie, S., Mori, G., Malik, J.: Matching with shape contexts. In: Krim, H., Yezzi, A. (eds.) Statistics and Analysis of Shapes, pp. 81–105. Springer, Heidelberg (2006). https://doi.org/10.1007/0-8176-4481-4_4
Bold, N., Zhang, C., Akashi, T.: 3D point cloud retrieval with bidirectional feature match. IEEE Access 7, 164194–164202 (2019)
Deng, H., Birdal, T., Ilic, S.: PPF-FoldNet: unsupervised learning of rotation invariant 3D local descriptors. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 620–638. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01228-1_37
Deng, H., Birdal, T., Ilic, S.: PPFNet: global context aware local features for robust 3D point matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 195–205 (2018)
Deng, H., Birdal, T., Ilic, S.: 3D local features for direct pairwise registration. In: Computer Vision and Pattern Recognition (CVPR). IEEE (2019)
Filipe, S., Alexandre, L.A.: A comparative evaluation of 3D keypoint detectors in a RGB-D object dataset. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), vol. 1, pp. 476–483. IEEE (2014)
Frome, A., Huber, D., Kolluri, R., Bülow, T., Malik, J.: Recognizing objects in range data using regional point descriptors. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3023, pp. 224–237. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24672-5_18
Guerrero, P., Kleiman, Y., Ovsjanikov, M., Mitra, N.J.: PCPNet learning local shape properties from raw point clouds. In: Computer Graphics Forum, vol. 37, pp. 75–85. Wiley Online Library (2018)
Hänsch, R., Weber, T., Hellwich, O.: Comparison of 3D interest point detectors and descriptors for point cloud fusion. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2(3), 57 (2014)
Harris, C.G., Stephens, M., et al.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 23.1–23.6 (1988)
Levoy, M., Gerth, J., Curless, B., Pull, K.: The Stanford 3D scanning repository (2005). http://www-graphics.stanford.edu/data/3Dscanrep
Li, J., Chen, B.M., Lee, G.H.: SO-Net: Self-organizing network for point cloud analysis. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9397–9406, June 2018
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)
Markuš, N., Pandžić, I., Ahlberg, J.: Learning local descriptors by optimizing the keypoint-correspondence criterion: applications to face matching, learning from unlabeled videos and 3D-shape retrieval. IEEE Trans. Image Process. 28(1), 279–290 (2019)
Mian, A., Bennamoun, M., Owens, R.: On the repeatability and quality of keypoints for local feature-based 3D object retrieval from cluttered scenes. Int. J. Comput. Vis. 89(2–3), 348–361 (2010). https://doi.org/10.1007/s11263-009-0296-z
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 5099–5108. Curran Associates, Inc. (2017)
Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: 2009 IEEE International Conference on Robotics and Automation, pp. 3212–3217. IEEE (2009)
Rusu, R.B., Blodow, N., Marton, Z.C., Beetz, M.: Aligning point cloud views using persistent feature histograms. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems,. pp. 3384–3391. IEEE (2008)
Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 9–13 May 2011
Salti, S., Tombari, F., Di Stefano, L.: SHOT: unique signatures of histograms for surface and texture description. Comput. Vis. Image Underst. 125, 251–264 (2014)
Sipiran, I., Bustos, B.: Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes. Vis. Comput. 27(11), 963 (2011). https://doi.org/10.1007/s00371-011-0610-y
Steder, B., Rusu, R.B., Konolige, K., Burgard, W.: Point feature extraction on 3D range scans taking into account object boundaries. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 2601–2608. IEEE (2011)
Tombari, F., Salti, S., Di Stefano, L.: Unique shape context for 3D data description. In: Proceedings of the ACM Workshop on 3D Object Retrieval, pp. 57–62 (2010)
Tombari, F., Salti, S., di Stefano, L.: Performance evaluation of 3D keypoint detectors. Int. J. Comput. Vis. 102(1–3), 198–220 (2013). https://doi.org/10.1007/s11263-012-0545-4
Vargas, J., Garcia, A., Oprea, S., Escolano, S., Rodriguez, J.: Object recognition pipeline: Grasping in domestic environments, pp. 18–33. IGI Global (2018)
Yew, Z.J., Lee, G.H.: 3DFeat-Net: weakly supervised local 3D features for point cloud registration. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 630–646. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_37
Zhong, Y.: Intrinsic shape signatures: a shape descriptor for 3D object recognition. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 689–696. IEEE (2009)
Acknowledgment
This work has been funded by Slovak Ministry of Education under contract VEGA 1/0796/00 and by the Charles University grant SVV-260588.
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Stancelova, P., Sikudova, E., Cernekova, Z. (2020). Performance Evaluation of Selected 3D Keypoint Detector–Descriptor Combinations. In: Chmielewski, L.J., Kozera, R., Orłowski, A. (eds) Computer Vision and Graphics. ICCVG 2020. Lecture Notes in Computer Science(), vol 12334. Springer, Cham. https://doi.org/10.1007/978-3-030-59006-2_17
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