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2020 | OriginalPaper | Buchkapitel

Normal Grouping Density Separation (NGDS): A Novel Object-Driven Indoor Point Cloud Partition Method

verfasst von : Jakub Walczak, Grzegorz Andrzejczak, Rafał Scherer, Adam Wojciechowski

Erschienen in: Computational Science – ICCS 2020

Verlag: Springer International Publishing

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Abstract

Precise segmentation/partition is an essential part of many point cloud processing strategies. In the state-of-the-art methods, either the number of clusters or expected supervoxel resolution needs to be carefully selected before segmentation. This makes these processes semi-supervised. The proposed Normal Grouping- Density Separation (NGDS) strategy, relying on both grouping normal vectors into cardinal directions and density-based separation, produces clusters of better (according to use quality measures) quality than current state-of-the-art methods for widely applied object-annotated indoor benchmark dataset. The method reaches, on average, lower under-segmentation error than VCCS (by 45.9pp), Lin et al. (by 14.8pp), and SSP (by 26.2pp). Another metric - achievable segmentation accuracy - yields 92.1% across the tested dataset what is higher value than VCCS (by 14pp), Lin et al. (by 3.8pp), and SSP (by 10.3pp). The experiment carried out indicates superiority of the proposed method as a partition/segmentation algorithm - a process being usually a preprocessing stage of many object detection workflows.

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https://github.com/yblin/Supervoxel-for-3D-point-clouds.

https://github.com/loicland/superpoint_graph.

Armeni, I., Sax, A., Zamir, A.R., Savarese, S.: Joint 2D–3D-semantic data for indoor scene understanding. ArXiv e-prints, February 2017

Ben-Shabat, Y., Lindenbaum, M., Fischer, A.: Nesti-Net: normal estimation for unstructured 3d point clouds using convolutional neural networks (2018). http://arxiv.org/abs/1812.00709

Bentley, J.L.: Multidimensional binary search trees used for associative searching. Comm. ACM 18(9), 509–517 (1975). https://doi.org/10.1145/361002.361007MathSciNetCrossRefMATH

Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: richly-annotated 3D reconstructions of indoor scenes. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR). IEEE (2017)

Dong, Z., Yang, B., Hu, P., Scherer, S.: An efficient global energy optimization approach for robust 3D plane segmentation of point clouds. ISPRS J. Phot. Rem. Sens. 137, 112–133 (2018). https://doi.org/10.1016/j.isprsjprs.2018.01.013CrossRef

El-Sayed, E., Abdel-Kader, R.F., Nashaat, H., Marei, M.: Plane detection in 3D point cloud using octree-balanced density down-sampling and iterative adaptive plane extraction. IET Image Proc. 12(9), 1595–1605 (2018). https://doi.org/10.1049/iet-ipr.2017.1076CrossRef

Forczmański, P., Nowosielski, A.: Multi-view data aggregation for behaviour analysis in video surveillance systems. In: Chmielewski, L.J., Datta, A., Kozera, R., Wojciechowski, K. (eds.) ICCVG 2016. LNCS, vol. 9972, pp. 462–473. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46418-3_41CrossRef

Guerrero, P., Kleiman, Y., Ovsjanikov, M., Mitra, N.J.: PCPNet learning local shape properties from raw point clouds. In: Computer Graphics Forum, vol. 37, no. 2, pp. 75–85 (2018). https://doi.org/10.1111/cgf.13343

Kasaei, S.H., Tomé, A.M., Seabra Lopes, L., Oliveira, M.: GOOD: a global orthographic object descriptor for 3D object recognition and manipulation. Pattern Recogn. Lett. 83, 312–320 (2016). https://doi.org/10.1016/j.patrec.2016.07.006CrossRef

10.

Kumar, A., Anders, K., Winiwarter, L., Höfle, B.: Feature relevance analysis for 3D point cloud classification using deep learning. ISPRS Ann. Phot. Rem. Sens. Spat. Inf. Sci. 4 (2019). https://doi.org/10.5194/isprs-annals-IV-2-W5-373-2019

11.

Landrieu, L., Boussaha, M.: Supervized Segmentation with Graph-Structured Deep Metric Learning. IEEE Geosci. Remote Sens. Mag. (2019). http://arxiv.org/abs/1905.04014

12.

Landrieu, L., Simonovsky, M.: Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE CVPR Conference, pp. 4558–4567 (2018)

13.

Lazarek, J., Pryczek, M.: A review on point cloud semantic segmentation methods. J. Appl. Comput. Sci. 26(2), 99–106 (2018)

14.

Lin, Y., Wang, C., Zhai, D., Li, W., Li, J.: Toward better boundary preserved supervoxel segmentation for 3D point clouds. ISPRS J. Phot. Rem. Sens. 143, 39–47 (2018). https://doi.org/10.1016/j.isprsjprs.2018.05.004CrossRef

15.

Liu, M.Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: CVPR 2011, pp. 2097–2104. IEEE (2011)

16.

Lu, R., Brilakis, I., Middleton, C.R.: Detection of structural components in point clouds of existing RC bridges. Comput.-Aided Civil Infrastruct. Eng. 34(3), 191–212 (2019). https://doi.org/10.1111/mice.12407CrossRef

17.

Ma, Y., Guo, Y., Lei, Y., Lu, M., Zhang, J.: 3DMAX-NET: a multi-scale spatial contextual network for 3D point cloud semantic segmentation. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 1560–1566. IEEE (2018)

18.

Maleika, W., Forczmański, P.: Adaptive modeling and compression of bathymetric data with variable density. IEEE J. Oceanic Eng. (2019). https://doi.org/10.1109/JOE.2019.2941120CrossRef

19.

McInnes, L., Healy, J.: Accelerated hierarchical density clustering. arXiv preprint arXiv:1705.07321 (2017)

20.

McInnes, L., Healy, J., Astels, S.: HDBSCAN: hierarchical density based clustering. J. Open Source Softw. 2(11), 205 (2017)CrossRef

21.

Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_54CrossRef

22.

Papon, J., Abramov, A., Schoeler, M., Worgotter, F.: Voxel cloud connectivity segmentation-supervoxels for point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2027–2034 (2013)

23.

Rusu, R.B., Cousins, S.: 3D is here: point cloud library. Point Cloud Library. http://pointclouds.org/. Accessed 15 2017

24.

Scherer, R.: Feature detection. In: Scherer, R. (ed.) Computer Vision Methods for Fast Image Classification and Retrieval. SCI, vol. 821, pp. 7–32. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12195-2_2CrossRef

25.

Sculley, D.: Web-scale k-means clustering. In: Proceedings of the 19th International Conference on WWW WWW 2010, pp. 1177–1178. ACM, New York (2010). https://doi.org/10.1145/1772690.1772862

26.

Thomas, H., Goulette, F., Deschaud, J.E., Marcotegui, B.: Semantic classification of 3d point clouds with multiscale spherical neighborhoods. In: 2018 International Conference on 3D Vision (3DV), pp. 390–398. IEEE (2018)

27.

Walczak, J., Wojciechowski, A.: Clustering quality measures for point cloud segmentation tasks. In: Chmielewski, L.J., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A.M., Petkov, N. (eds.) ICCVG 2018. LNCS, vol. 11114, pp. 173–186. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00692-1_16CrossRef

28.

Walewski, P., Gałaj, T., Szajerman, D.: Heuristic based real-time hybrid rendering with the use of rasterization and ray tracing method. Open Phys. 17(1), 527–544 (2019). https://doi.org/10.1515/phys-2019-0055CrossRef

29.

Wang, X., Liu, S., Shen, X., Shen, C., Jia, J.: Associatively segmenting instances and semantics in point clouds (2019)

30.

Wawrzonowski, M., Szajerman, D.: Optimization of screen-space directional occlusion algorithms. Open Phys. 17(1), 519–526 (2019). https://doi.org/10.1515/phys-2019-0054CrossRef

31.

Wróblewski, A., Andrzejczak, J.: Wave propagation time optimization for geodesic distances calculation using the heat method. Open Phys. 17(1), 263–275 (2019)CrossRef

32.

Xie, Y., Tian, J., Zhu, X.X.: A review of point cloud semantic segmentation (2019). http://arxiv.org/abs/1908.08854

33.

Xu, Y., Yao, W., Hoegner, L., Stilla, U.: Segmentation of building roofs from airborne LiDAR point clouds using robust voxel-based region growing. Remote Sens. Lett. 8(11), 1062–1071 (2017). https://doi.org/10.1080/2150704X.2017.1349961CrossRef

34.

Yan, J., Shan, J., Jiang, W.: A global optimization approach to roof segmentation from airborne lidar point clouds. ISPRS J. Phot. Rem. Sens. 94, 183–193 (2014). https://doi.org/10.1016/j.isprsjprs.2014.04.022CrossRef

35.

Yang, F., et al.: Automatic indoor reconstruction from point clouds in multi-room environments with curved walls. Sensors 19(17), 3798 (2019). https://doi.org/10.3390/s19173798CrossRef

36.

Yin, Z., Liu, Z., Zhou, L., Zhang, F., Fu, K., Kong, X.: Superpixel based continuous conditional random field neural network for semantic segmentation. Neurocomputing 340, 196–210 (2019). https://doi.org/10.1016/j.neucom.2019.01.016CrossRef

37.

Yousefhussien, M., Kelbe, D.J., Ientilucci, E.J., Salvaggio, C.: A multi-scale fully convolutional network for semantic labeling of 3D point clouds. ISPRS J. Phot. Rem. Sens. 143, 191–204 (2018). https://doi.org/10.1016/j.isprsjprs.2018.03.018CrossRef

38.

Zhao, J., Liu, C., Zhang, B.: PLSTMNet: a new neural network for segmentation of point cloud. In: 2018 11th International Workshop on Human Friendly Robotics (HFR), pp. 42–47 (2019). https://doi.org/10.1109/hfr.2018.8633482

Titel: Normal Grouping Density Separation (NGDS): A Novel Object-Driven Indoor Point Cloud Partition Method
verfasst von: Jakub Walczak
Grzegorz Andrzejczak
Rafał Scherer
Adam Wojciechowski
Verlag: Springer International Publishing
Buch: Computational Science – ICCS 2020
Print ISBN: 978-3-030-50432-8

Electronic ISBN: 978-3-030-50433-5

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-50433-5_8

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