Top

Published in:

2020 | OriginalPaper | Chapter

Fast 3D Scene Segmentation and Partial Object Retrieval Using Local Geometric Surface Features

Authors : Dimitrios Dimou, Konstantinos Moustakas

Published in: Transactions on Computational Science XXXVI

Publisher: Springer Berlin Heidelberg

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Robotic vision and in particular 3D understanding has attracted intense research efforts the last few years due to its wide range of applications, such as robot-human interaction, augmented and virtual reality etc, and the introduction of low-cost 3D sensing devices. In this paper we explore one of the most popular problems encountered in 3D perception applications, namely the segmentation of a 3D scene and the retrieval of similar objects from a model database. We use a geometric approach for both the segmentation and the retrieval modules that enables us to develop a fast, low-memory footprint system without the use of large-scale annotated datasets. The system is based on the fast computation of surface normals and the encoding power of local geometric features. Our experiments demonstrate that such a complete 3D understanding framework is possible and advantages over other approaches as well as weaknesses are discussed.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter An Immersive Virtual Environment for Visualization of Complex and/or Infinitely Distant Territory

next chapter Hybrid Nature-Inspired Optimization Techniques in Face Recognition

Our source code will be available on Github upon publication.

https://www.acin.tuwien.ac.at/en/vision-for-robotics/software-tools/osd/

http://hampson.cast.uark.edu/

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

Boulch, A., Saux, B.L., Audebert, N.: Unstructured point cloud semantic labeling using deep segmentation networks. In: Pratikakis, I., Dupont, F., Ovsjanikov, M. (eds.) Eurographics Workshop on 3D Object Retrieval. The Eurographics Association (2017). https://doi.org/10.2312/3dor.20171047

Bronstein, M.M., Kokkinos, I.: Scale-invariant heat kernel signatures for non-rigid shape recognition. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1704–1711, June 2010. https://doi.org/10.1109/CVPR.2010.5539838

Dimou, D., Moustakas, K.: A framework for 3D object segmentation and retrieval using local geometric surface features. In: International Conference CyberWorlds, October 2018

Ecins, A., Fermüller, C., Aloimonos, Y.: Cluttered scene segmentation using the symmetry constraint. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 2271–2278, May 2016. https://doi.org/10.1109/ICRA.2016.7487376

Engelmann, F., Kontogianni, T., Hermans, A., Leibe, B.: Exploring spatial context for 3D semantic segmentation of point clouds. CoRR abs/1802.01500 (2018). http://arxiv.org/abs/1802.01500

Firman, M.: RGBD datasets: past, present and future. CoRR abs/1604.00999 (2016). http://arxiv.org/abs/1604.00999

10.

Fleishman, S., Drori, I., Cohen-Or, D.: Bilateral mesh denoising. ACM Trans. Graph. 22(3), 950–953 (2003). https://doi.org/10.1145/882262.882368CrossRef

11.

Grard, M., Brégier, R., Sella, F., Dellandréa, E., Chen, L.: Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network. CoRR abs/1801.01281 (2018). http://arxiv.org/abs/1801.01281

12.

Hackel, T., Wegner, J., Schindler, K.: Fast semantic segmentation of 3D point clouds with strongly varying density. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. III–3, 177–184 (2016)CrossRef

13.

He, X., Zhou, Y., Zhou, Z., Bai, S., Bai, X.: Triplet-center loss for multi-view 3D object retrieval (2018)

14.

Holz, D., Behnke, S.: Approximate triangulation and region growing for efficient segmentation and smoothing of range images. Robot. Auton. Syst. 62(9), 1282–1293 (2014). https://doi.org/10.1016/j.robot.2014.03.013CrossRef

15.

Huang, J., You, S.: Point cloud labeling using 3D convolutional neural network. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2670–2675, December 2016. https://doi.org/10.1109/ICPR.2016.7900038

16.

Kim, E., Medioni, G.: 3D object recognition in range images using visibility context. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3800–3807, September 2011. https://doi.org/10.1109/IROS.2011.6094527

17.

Moustakas, K., Stavropoulos, G., Tzovaras, D.: Protrusion fields for 3D model search and retrieval based on range image queries. In: Bebis, G., et al. (eds.) ISVC 2012. LNCS, vol. 7431, pp. 610–619. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33179-4_58CrossRef

18.

Newcombe, R.A.: KinectFusion: real-time dense surface mapping and tracking. In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality, pp. 127–136, October 2011. https://doi.org/10.1109/ISMAR.2011.6092378

19.

Papadakis, P., Pratikakis, I., Theoharis, T., Perantonis, S.: PANORAMA: a 3D shape descriptor based on panoramic views for unsupervised 3D object retrieval. Int. J. Comput. Vis. 89(2), 177–192 (2010). https://doi.org/10.1007/s11263-009-0281-6CrossRef

20.

Pratikakis, I., et al.: SHREC’16 track: partial shape queries for 3D object retrieval (2016)

21.

Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. CoRR abs/1612.00593 (2016). http://arxiv.org/abs/1612.00593

22.

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, May 2009. https://doi.org/10.1109/ROBOT.2009.5152473

23.

Rusu, R., Marton, Z., Blodow, N., Beetz, M.: Persistent point feature histograms for 3D point clouds, vol. 16, January 2008

24.

Rusu, R.B., Marton, Z.C., Blodow, N., Dolha, M., Beetz, M.: Towards 3D point cloud based object maps for household environments. Robot. Auton. Syst. 56(11), 927–941 (2008). https://doi.org/10.1016/j.robot.2008.08.005CrossRef

25.

Savelonas, M.A., Pratikakis, I., Sfikas, K.: An overview of partial 3D object retrieval methodologies. Multimedia Tools Appl. 74(24), 11783–11808 (2014). https://doi.org/10.1007/s11042-014-2267-9CrossRef

26.

Savelonas, M.A., Pratikakis, I., Sfikas, K.: Fisher encoding of differential fast point feature histograms for partial 3D object retrieval. Pattern Recogn. 55, 114–124 (2016). https://doi.org/10.1016/j.patcog.2016.02.003. http://www.sciencedirect.com/science/article/pii/S0031320316000595CrossRef

27.

Sfikas, K., Pratikakis, I., Koutsoudis, A., Savelonas, M., Theoharis, T.: Partial matching of 3D cultural heritage objects using panoramic views. Multimedia Tools Appl. 75(7), 3693–3707 (2014). https://doi.org/10.1007/s11042-014-2069-0CrossRef

28.

Sfikas, K., Theoharis, T., Pratikakis, I.: Exploiting the PANORAMA representation for convolutional neural network classification and retrieval, April 2017

29.

Shi, B., Bai, S., Zhou, Z., Bai, X.: DeepPano: deep panoramic representation for 3-D shape recognition. IEEE Signal Process. Lett. 22, 2339–2343 (2015)CrossRef

30.

Sinha, A., Bai, J., Ramani, K.: Deep learning 3D shape surfaces using geometry images. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 223–240. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_14CrossRef

31.

Stavropoulos, G., Moschonas, P., Moustakas, K., Tzovaras, D., Strintzis, M.G.: 3-D model search and retrieval from range images using salient features. IEEE Trans. Multimedia 12(7), 692–704 (2010). https://doi.org/10.1109/TMM.2010.2053023CrossRef

32.

Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: Multi-view convolutional neural networks for 3D shape recognition. CoRR abs/1505.00880 (2015). http://arxiv.org/abs/1505.00880

33.

Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: Proceedings of the Symposium on Geometry Processing, SGP 2009, pp. 1383–1392. Eurographics Association, Aire-la-Ville (2009). http://dl.acm.org/citation.cfm?id=1735603.1735621

34.

Tateno, K., Tombari, F., Navab, N.: Real-time and scalable incremental segmentation on dense slam. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4465–4472, September 2015. https://doi.org/10.1109/IROS.2015.7354011

35.

Vosselman, G.: Point cloud segmentation for urban scene classification. Int. Soc. Photogramm. Remote Sens. (ISPRS) 1, 257–262 (2013). https://doi.org/10.5194/isprsarchives-XL-7-W2-257-2013CrossRef

36.

Wang, W., Yu, R., Huang, Q., Neumann, U.: SGPN: similarity group proposal network for 3D point cloud instance segmentation. CoRR abs/1711.08588 (2017). http://arxiv.org/abs/1711.08588

37.

Wang, Y., Shi, T., Yun, P., Tai, L., Liu, M.: PointSeg: real-time semantic segmentation based on 3D LiDAR point cloud. ArXiv e-prints, July 2018

38.

Whelan, T., Kaess, M., Johannsson, H., Fallon, M., Leonard, J.J., McDonald, J.: Real-time large-scale dense RGB-D SLAM with volumetric fusion. Int. J. Robot. Res. 34(4–5), 598–626 (2015). https://doi.org/10.1177/0278364914551008CrossRef

39.

Xiong, X., Munoz, D., Bagnell, J.A., Hebert, M.: 3-D scene analysis via sequenced predictions over points and regions. In: 2011 IEEE International Conference on Robotics and Automation, pp. 2609–2616, May 2011. https://doi.org/10.1109/ICRA.2011.5980125

40.

Yücer, K., Sorkine-Hornung, A., Wang, O., Sorkine-Hornung, O.: Efficient 3D object segmentation from densely sampled light fields with applications to 3D reconstruction. ACM Trans. Graph. 35(3), 22:1–22:15 (2016). https://doi.org/10.1145/2876504CrossRef

41.

Ückermann, A., Haschke, R., Ritter, H.: Real-time 3D segmentation of cluttered scenes for robot grasping. In: 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pp. 198–203 (2012)

Title: Fast 3D Scene Segmentation and Partial Object Retrieval Using Local Geometric Surface Features
Authors: Dimitrios Dimou
Konstantinos Moustakas
Publisher: Springer Berlin Heidelberg
Book: Transactions on Computational Science XXXVI
Print ISBN: 978-3-662-61363-4

Electronic ISBN: 978-3-662-61364-1

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-662-61364-1_5

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner