The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
SHOT: Unique signatures of histograms for surface and texture description
Abstract
Abstract This paper presents a local 3D descriptor for surface matching dubbed SHOT. Our proposal stems from a taxonomy of existing methods which highlights two major approaches, referred to as Signatures and Histograms, inherently emphasizing descriptiveness and robustness respectively. We formulate a comprehensive proposal which encompasses a repeatable local reference frame as well as a 3D descriptor, the latter featuring an hybrid structure between Signatures and Histograms so as to aim at a more favorable balance between descriptive power and robustness. A quite peculiar trait of our method concerns seamless integration of multiple cues within the descriptor to improve distinctiveness, which is particularly relevant nowadays due to the increasing availability of affordable RGB-D sensors which can gather both depth and color information. A thorough experimental evaluation based on datasets acquired with different types of sensors, including a novel RGB-D dataset, vouches that SHOT outperforms state-of-the-art local descriptors in experiments addressing descriptor matching for object recognition, 3D reconstruction and shape retrieval.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · cited 1×
“Finally, [24] extends the successful SURF descriptor [25] to 3D data, by voxelizing the 3D mesh so as to efficiently compute Haar wavelets and use their responses as signature trait.”
MethodA performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Face recognition: A literature survey
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003) · ACM Computing Surveys · doi:10.1145/954339.954342 · Cited by 6,163
Fast Point Feature Histograms (FPFH) for 3D registration
Rusu, R. B., Blodow, N., & Beetz, M. (2009) · 2009 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2009.5152473 · Cited by 3,878
PCA-SIFT: a more distinctive representation for local image descriptors
Ke, Y., & Sukthankar, R. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315206 · Cited by 3,237
Surface reconstruction from unorganized points
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Using spin images for efficient object recognition in cluttered 3D scenes
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KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al. (2011) · Proceedings of the 24th annual ACM symposium on User interface software and technology · doi:10.1145/2047196.2047270 · Cited by 2,110
A metric for distributions with applications to image databases
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Cited by
Fully Convolutional Geometric Features
Choy, C., Park, J., & Koltun, V. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00905 · Cited by 759
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
Deng, H., Birdal, T., & Ilic, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00028 · Cited by 631
Tangent Convolutions for Dense Prediction in 3D
Tatarchenko, M., Park, J., Koltun, V., & Zhou, Q.-Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00409 · Cited by 593
A Comprehensive Performance Evaluation of 3D Local Feature Descriptors
Guo, Y., Bennamoun, M., Sohel, F., Lu, M., Wan, J., & Kwok, N. M. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-015-0824-y · Cited by 555
BOP: Benchmark for 6D Object Pose Estimation
Hodaň, T., Michel, F., Brachmann, E., Kehl, W., Buch, A. G., Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., et al. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01249-6_2 · Cited by 470
Self-citation
Metadata
- Authors
- Salti, S., Tombari, F., & Di Stefano, L.
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2014-08
- Volume
- 125
- Pages
- 251-264
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W1972485825
- Cited by
- 987
- References
- 57
- FWCI
- 29.69
- Type
- journal article
- Language
- en
- Open access
- closed
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