Abstract
In this paper, we present a novel 3D descriptor that bridges the gap between global and local approaches. While local descriptors proved to be a more attractive choice for object recognition within cluttered scenes, they remain less discriminating exactly due to the limited scope of the local neighborhood. On the other hand, global descriptors can better capture relationships between distant points, but are generally affected by occlusions and clutter. So, we propose the Local-to-Global Signature (LGS) descriptor, which relies on surface point classification together with signature-based features to overcome the drawbacks of both local and global approaches. As our tests demonstrate, the proposed LGS can capture more robustly the exact structure of the objects while remaining robust to clutter and occlusion and avoiding sensitive, low-level features, such as point normals. The tests performed on four different datasets demonstrate the robustness of the proposed LGS descriptor when compared to three of the SOTA descriptors today: SHOT, Spin Images and FPFH. In general, LGS outperformed all three descriptors and for some datasets with a 50–70% increase in Recall.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aldoma, A., Marton, Z.C., Tombari, F., Wohlkinger, W., Potthast, C., Zeisl, B., Rusu, R.B., Gedikli, S., Vincze, M.: Tutorial: Point cloud library: three-dimensional object recognition and 6 dof pose estimation. IEEE Robot. Automat. Mag. 19, 80–91 (2012)
Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 356–369. Springer, Heidelberg (2010)
Rusu, R.B., Marton, Z.C., Blodow, N., Beetz, M.: Persistent point feature histograms for 3d point clouds. In: Proceedings of the 10th International Conference on Intelligent Autonomous Systems (IAS-10), Baden-Baden, Germany (2008)
Rusu, R., Blodow, N., Beetz, M.: Fast point feature histograms (fpfh) for 3d registration. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 3212–3217 (2009)
Frome, A., Huber, D., Kolluri, R., Bülow, T., Malik, J.: Recognizing objects in range data using regional point descriptors. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3023, pp. 224–237. Springer, Heidelberg (2004)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24, 509–522 (2002)
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. ACM, New York (2010)
Chua, C.S., Jarvis, R.: Point signatures: a new representation for 3d object recognition. Int. J. Comput. Vision 25, 63–85 (1997)
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, 348–361 (2010)
Rusu, R., Bradski, G., Thibaux, R., Hsu, J.: Fast 3d recognition and pose using the viewpoint feature histogram. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2155–2162 (2010)
Rusu, R.B., Holzbach, A., Bradski, G., Beetz, M.: Detecting and segmenting objects for mobile manipulation. In: Proceedings of IEEE Workshop on Search in 3D and Video (S3DV), held in conjunction with the 12th IEEE International Conference on Computer Vision (ICCV), Kyoto, Japan (2009)
Marton, Z.C., Pangercic, D., Blodow, N., Beetz, M.: Combined 2D–3D categorization and classification for multimodal perception systems. Int. J. Robot. Res. 30, 1378–1402 (2011)
Madry, M., Ek, C., Detry, R., Hang, K., Kragic, D.: Improving generalization for 3d object categorization with global structure histograms. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1379–1386 (2012)
Tombari, F., Salti, S., Di Stefano, L.: Performance evaluation of 3d keypoint detectors. Int. J. Comput. Vis. 102, 198–220 (2013)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1615–1630 (2005)
Ke, Y., Sukthankar, R.: Pca-sift: A more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR’04, pp. 506–513. IEEE Computer Society (2004)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hadji, I., DeSouza, G.N. (2015). Local-to-Global Signature Descriptor for 3D Object Recognition. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-16628-5_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16627-8
Online ISBN: 978-3-319-16628-5
eBook Packages: Computer ScienceComputer Science (R0)